Publications by Alex A. Freitas

Research-Oriented Books
  • R.C. Barros, A.C.P.L.F. de Carvalho, A.A. Freitas. Automatic Design of Decision-Tree Induction Algorithms. Springer, 2015. xii + 176 pages. Publisher's webpage about the book

  • G.L.Pappa and A.A. Freitas. Automating the Design of Data Mining Algorithms: an Evolutionary Computation Approach. Springer, 2010. xiii + 187 pages. Publisher's webpage about the book

  • A.A. Freitas. Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer, 2002. xiv + 264 pages. Book Cover   Table of Contents

  • A.A. Freitas and S.H. Lavington. Mining Very Large Databases with Parallel Processing. Kluwer, 1998. ix + 208 pages. Table of Contents and Publisher's address


    • Papers in Journals, Conference Proceedings, and Book Chapters

    2024

    C. Ribeiro, A.A. Freitas. A lexicographic optimisation approach to promote more recent features on longitudinal decision-tree-based classifiers: applications to the English Longitudinal Study of Ageing. Artificial Intelligence Review 57, Article Number 84, 29 pages. 2024. (link to open access paper)

    2023

    C. Ribeiro, C.K. Farmer, J.P. de Magalhaes, A.A. Freitas. Predicting lifespan-extending chemical compounds for C. elegans with machine learning and biologically interpretable features. Aging (Albany, NY) (abbreviated as Aging-US in the Web of Science), 15(3), 6073-6099, 15 July 2023. (link to open access paper)

    H.E.L. Cagnini, S.C.N. Das Dores, A.A. Freitas, R.C. Barros. A survey of evolutionary algorithms for supervised ensemble learning. The Knowledge Engineering Review, 38(e1), 1-43, 2023. (pdf)

    2022

    G.D.V. Magdaleno, V. Bespalov, Y. Zheng, A.A. Freitas, J.P. de Magalhaes. Machine learning-based predictions of dietary restriction associations across ageing-related genes. BMC Bioinformatics, 23:10, 2022. DOI: https://doi.org/10.1186/s12859-021-04523-8. (pdf)

    M.R.H. Maia, A. Plastino, A.A. Freitas, J.P. de Magalhaes. Interpretable ensembles of classifiers for uncertain data with bioinformatics applications. IEEE/ACM Transactions on Computational Biology and Bioinformatics, (unformatted version) online in advance, 2022. (pdf)

    J. Brookhouse and A.A. Freitas. Fair feature selection with a lexicographic multi-objective genetic algorithm. In: Parallel Problem Solving from Nature – PPSN XVII, PPSN 2022 (Proceedings of the 17th International Conference). Lecture Notes in Computer Science, Vol. 13399, 151-163. Springer, Cham, 2022. (pdf)

    T. Pomsuwan and A.A. Freitas. New variations of random survival forests and applications to age-related disease data. In: Proceedings of the 2022 10th IEEE International Conference on Healthcare Informatics (ICHI-2022), 1-10. IEEE Computer Society – Conference Publishing Services, 2022. (pdf)

    J.D. Saunders and A.A. Freitas. GA-Auto-PU: a genetic algorithm-based automated machine learning system for positive-unlabeled learning. In: Proceedings of the GECCO’22 Companion (Genetic and Evolutionary Computation Conference), 288-291. ACM Press, 2022. (pdf)

    J.D. Saunders and A.A. Freitas. Evaluating a new genetic algorithm for automated machine learning in positive-unlabelled learning. In: Proceedings of the 15th International Conference on Artificial Evolution (Evolution Artificielle) (EA 2022), Lecture Notes in Computer Science, Vol. 14091, 42-57. Springer, Cham, 2022. (pdf)

    2021

    M.R.H. Maia, A. Plastino and A.A. Freitas. An ensemble of naive Bayes classifiers for uncertain categorical data. In: Proceedings of the 21st IEEE International Conference on Data Mining (ICDM 2021), 1216-1221. (pdf)

    C. Ribeiro and A.A. Freitas. Constructed temporal features for longitudinal classification of human ageing data. In: Proceedings of the 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), 106-112. (pdf)

    D. Palmer, F. Fabris, A. Doherty, A.A. Freitas, J.P. de Magalhaes. Ageing transcriptome meta-analysis reveals similarities between key mammalian tissues. Aging (Albany, NY) (by PubMed), Aging-US (by Web of Science), www.aging-us.com, 13(3), 3313-3341, 2021. DOI: https://doi.org/10.18632/aging.202648 (pdf)

    C. Ribeiro and A.A. Freitas. A data-driven missing value imputation approach for longitudinal datasets. AI Review, published online on 06/03/2021. 30 pages. (unformatted version) (pdf)

    N.M. Neumann, A. Plastino, J.A. Pinto-Junior, A.A. Freitas. Is p-value < 0.05 enough? A study on the statistical evaluation of classifiers. Knowledge Engineering Review, 36, e1, Nov. 2020. 26 pages. Published Online: 27/11/2020. DOI: https://doi.org/10.1017/S0269888920000417 (pdf)

    2020

    M. Basgalupp, R. Barros, A. de Sa, G. Pappa, R. Mantovani, A.C.P.L.F. de Carvalho, A.A. Freitas. An extensive experimental evaluation of automated machine learning methods for recommending classification algorithms. Evolutionary Intelligence, 20 pages. Published online: 19/08/2020. DOI: https://doi.org/10.1007/s12065-020-00463-z (pdf)

    P.N. da Silva, A. Plastino, A.A. Freitas. Prioritizing positive feature values: a new hierarchical feature selection method. Applied Intelligence. (unformatted version) Published online: 20/07/2020. DOI: https://doi.org/10.1007/s10489-020-01782-5. (pdf)

    P.N. da Silva, A. Plastino, F. Fabris, A.A. Freitas. A novel feature selection method for uncertain features: an application to the prediction of pro-/anti-longevity genes. IEEE/ACM Transactions on Computational Biology and Bioinformatics. (unformatted version) Published online: 20/04/2020. DOI: 10.1109/TCCB.2020.2988450. (pdf)

    F. Fabris, D. Palmer, K.M. Salama, J.P. de Magalhaes, A.A. Freitas. Using deep learning to associate human genes with age-related diseases. Bioinformatics, 36(7), 2202-2208, 01 April 2020. (link to open access paper)

    J.C. Xavier-Junior, A.A. Freitas, T.B. Ludermir, A. Feitosa-Neto, C.A.S. Barreto. An evolutionary algorithm for automated machine learning focusing on classifier ensembles: an improved algorithm and extended results. Journal of Theoretical Computer Science, 805, 1-18, 18 Jan. 2020. (unformatted version) DOI: https://doi.org/10.1016/j.tcs.2019.12.002 (pdf)

    C. Ribeiro and A.A. Freitas. A New Random Forest Method for Longitudinal Data Classification Using a Lexicographic Bi-Objective Approach. In: Proc. 2020 IEEE Symposium Series on Computational Intelligence (SSCI 2020), Article 223, 8 pages (pdf)

    T. Pomsuwan and A.A. Freitas. Adapting random forests to cope with heavily censored datasets in survival analysis. In: Proc. 2020 European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2020), 697-702. (pdf)

    A.G.C. de Sa, C.G. Pimenta, G.L. Pappa and A.A. Freitas. A robust experimental evaluation of automated multi-label classification methods. In: Proc. 2020 Genetic and Evolutionary Computation Conference (GECCO-20), 175-183. ACM Press, 2020. (pdf)

    2019

  • S. Ovchinnik, F.E.B. Otero, A.A. Freitas. Monotonicity detection and enforcement in longitudinal classification. In: Artificial Intelligence XXXVI: Proc. 39th SGAI International Conference on Artificial Intelligence, AI 2019, Lecture Notes in Artificial Intelligence 11927, 63-77. Springer, 2019. (pdf)

  • N. Zhou et al (167 authors in total). The CAFA challenge reports improved protein function prediction and new functional annotations for hundres of genes through experimental screens. Genome Biology 20:244, 2019, 23 pages. (link to open access paper)

  • F. Fabris and A.A. Freitas. Analysing the overfit of the auto-sklearn automated machine learning tool. In Proc. of the 5th International Conference on Machine Learning, Optimization and Data Science (LOD 2019), Lecture Notes in Computer Science 11943, 508-520.Springer, 2019. (pdf)

  • A.A. Freitas. Automated machine learning for studying the trade-off between predictive accuracy and interpretability. In: Proc. Third IFIP International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE 2019), Lecture Notes in Computer Science 11713, 48-66. Springer, 2019. (pdf)

  • D. Tighe, T. Lewis-Morris and A. Freitas. Machine learning methods applied to audit of surgical outcomes after treatment for cancer of the head and neck. British Journal of Oral and Maxillofacial Surgery 57, 771-777, 2019 (unformatted version). DOI: 10.1016/j.bjoms.2019.05.026. (pdf)

  • F. Fabris, D. Palmer, J.P. de Magalhaes and A.A. Freitas. Comparing enrichment analysis and machine learning for identifying gene propertie that discriminate betweeen gene classes. Briefings in Bioinformatics, Advance Access, 20 March, 2019 (unformatted version). DOI: 10.1093/bib/bbz028. (pdf)

  • C. Ribeiro and A.A. Freitas. A mini-survey of supervised machine learning approaches for coping with ageing-related longitudinal datasets. To appear in Proc. 3rd Workshop on AI for Aging, Rehabilitation and Independent Assisted Living (ARIAL), held as part of IJCAI-2019. 5 pages. (pdf)

  • C. Ribeiro and A.A. Freitas. Comparing the effectiveness of six missing value imputation methods for longitudinal classification datasets. To appear in Proc. 3rd Workshop on AI for Aging, Rehabilitation and Independent Assisted Living (ARIAL), held as part of IJCAI-2019. 5 pages. (pdf)

  • A.A. Freitas. Investigating the role of Simpson's paradox in the analysis of top-ranked features in high-dimensional bioinformatics datasets. Briefings in Bioinformatics, Advance Access, Jan. 2019 (unformatted version) (pdf)
    Programs and datasets used in the experiments: (READ_ME file), (Datasets), (Perl programs), (Output files)

    2018

  • A.G.C. de Sa, A.A. Freitas, G.L. Pappa. Automated selection and configuration of multi-label classification algorithms with grammar-based genetic programming. In: Proc. 15th International Conference on Parallel Problem Solving from Nature (PPSN XV), Part II – Lecture Notes on Computer Science 11102, 308-320. Springer, 2018. (pdf)

  • J.C. Xavier-Junior, A.A. Freitas, A. Feitosa-Neto, T.B. Ludermir. A novel evolutionary algorithm for automated machine learning focusing on classiier ensembles. In: Proc. 7th Brazilian Conference on Intelligent Systems (BRACIS), 6 pages. 2018. (pdf)

  • F. Fabris, A. Doherty, D. Palmer, J.P. de Magalhaes, A.A. Freitas. A new approach for interpreting random forest models and its application to the biology of ageing. Bioinformatics, 34(14), 2449-2456. 2018. (pdf)

  • E.C. Goncalves, A.A. Freitas, A. Plastino. A survey of genetic algorithms for multi-label classification. In: Proc. Congress on Evolutionary Computation (CEC 2018), 981-988. IEEE. (pdf)

  • P.N. da Silva, A. Plastino, A.A. Freitas. A Novel Genetic Algorithm for Feature Selection in Hierarchical Feature Spaces. In: Proc. 2018 SIAM International Conference on Data Mining (SDM18), 738-746. SIAM, 2018. (pdf)

  • L.L. de Oliveira, A.A. Freitas and R. Tinos. Multi-objective genetic algorithms in the study of the genetic code's adaptability. Information Sciences 425, 48-61, 2018. (pdf)

    2017

  • T. Pomwusan and A.A. Freitas. Feature selection for the classification of longitudinal human ageing data. In: Proceedings of the 2017 IEEE International Conference on Data Mining Workshops (ARIAL workshop), 739-746. IEEE Computer Society, 2017. (pdf)

  • I. Martire, P.N. da Silva, A. Plastino, F. Fabris and A.A. Freitas. A novel probabilistic Jaccard distance measure for classification of sparse and uncertain data. In: Proc. 5th Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2017), 81-88. Sociedade Brasileira de Computacao, 2017. (pdf)

  • D.G. Barardo, D. Newby, D. Thornton, T. Ghafourian, J.P. de Magalhaes and A.A. Freitas. Machine learning for predicting lifespan-extending chemical compounds. Aging (Albany NY), 9(7), 1721-1737, 2017. (link for open access paper)

  • F. Fabris, J.P. de Magalhaes, A.A. Freitas. A review of supervised machine learning applied to ageing research. Biogerontology, 18(2), 171-188, April 2017. (pre-print version) (pdf) (webpage for bibliography in this area)

  • C.E. Ribeiro, L.H.S. Brito, C.N. Nobre, A.A. Freitas, L.E. Zarate. A revision and analysis of the comprehensiveness of the main longitudinal studies of human ageing for data mining research. In: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2017. (pre-print version) (pdf)

  • A.G.C. de Sa, G.L. Pappa, A.A. Freitas. Towards a method for automatically selecting and configuring multi-label classification algorithms. In: GECCO’17 Companion: Proceedings of the 2017 Genetic and Evolutionary Computation Conference Companion (7th Workshop on Evolutionary Computation for the Automated Design of Algorithms), 1125-1132. ACM Press, 2017. (pdf)

    2016

  • F. Fabris, A.A. Freitas. New KEGG pathway-based interpretable features for classifying ageing-related mouse proteins. Bioinformatics, 32(19), 2988-2995, October 1, 2016. (pre-print version) (pdf) (Datasets Used in the Experiments)

  • F. Fabris, A.A. Freitas, J.M.A. Tullet. An extensive empirical comparison of probabilistic hierarchical classifiers in datasets of ageing-related genes. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 13(6), 1045-1058, Nov./Dec. 2016. (pre-print version) (pdf)

  • M. Fernandes, C. Wan, R. Tacutu, D. Barardo, A. Rajput, J. Wang, H. Thoppil, D. Thornton, C. Yang, A.A. Freitas, J.P. de Magalhaes. Systematic analysis of the gerontome reveals links between aging and age-related diseases. Human Molecular Genetics, 25(21), 4804-4818, 2016. (pre-print version) (pdf)

  • N. Aniceto, A.A. Freitas, A. Bender, T. Ghafourian. A novel applicability domain technique for mapping predictive reliability across the chemical space of a QSAR: reliability-density neighbourhood. Journal of Cheminformatics, 8:69, 2016. 20 pages. (pdf)

  • N. Aniceto, A.A. Freitas, A. Bender, T. Ghafourian. Simultaneous prediction of four ATP-binding cassette transporters' substrates using multi-label QSAR. Molecular Informatics, 35(10), 514-528, Oct. 2016. (pre-print version) (pdf)

  • S. Cramer, M. Kampouridis, A.A. Freitas. A Genetic Decomposition Algorithm for Predicting Rainfall within Financial Weather Derivatives. In: Proceedings of the 2016 Genetic and Evolutionary Computation Conference (GECCO-2016), 885-892. ACM Press. (pdf)

    2015

  • C. Wan, A.A. Freitas, J.P. de Magalhaes. Predicting the pro-longevity or anti-longevity effect of model organism genes with new hierarchical feature selection methods. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 12(2), 262-275, March/April 2015. (pre-print version) (pdf) (Datasets Used in the Experiments)

  • C. Wan and A.A. Freitas. Two methods for constructing a gene ontology-based feature network for a bayesian network classifier and applications to datasets of aging-related genes. In: Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics (BCB-2015), 27-36. ACM Press. (pdf)

  • F. Fabris and A.A. Freitas. A novel extended hierarchical dependence network method based on non-hierarchical predictive classes and applications to ageing-related data. In Proceedings of the 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI-2015), 294-301. CPS Press. (pdf)

  • A.A. Freitas, K. Limbu, T. Ghafourian. Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients. Journal of Cheminformatics, 7(6), 2015. 17 pages. (pdf)

  • K.M. Salama and A.A. Freitas. Ant colony algorithms for constructing Bayesian multi-net classifiers. Intelligent Data Analysis, 19(2), 233-257. 2015. (pre-print version) (pdf)

  • R. Cerri, G.L. Pappa, A.C.P.L. de Carvalho, A.A. Freitas. An extensive evaluation of decision tree-based hierarchical multilabel classification methods and performance measures. Computational Intelligence, 31(1), 1-46, Feb. 2015. (pre-print version) (pdf)

  • D. Newby, A.A. Freitas, T. Ghafourian. Comparing Multi-Label Classification Methods for Provisional Biopharmaceutics Class Prediction. Molecular Pharmaceutics, 12 (1), 87–102, 5 Jan. 2015. (pre-print version) (pdf)

  • D. Newby, A.A. Freitas, T. Ghafourian. Decision trees to characterise the roles of permeability and solubility on the prediction of oral absorption. European Journal of Medicinal Chemistry, 90, 751-765, 27 Jan. 2015. (pre-print version) (pdf)

  • E.C. Goncalves, A. Plastino, A.A. Freitas. Simpler is better: a novel genetic algorithm to induce compact multi-label chain classifiers. In Proceedings of the 2015 Conference on Genetic and Evolutionary Computation Conference (GECCO-2015). 559-566. (pdf)

  • S. Jungjit and A.A. Freitas. In: GECCO-15Â’ Companion (Proceedings of the Workshop on Evolutionary Rule Learning at the 2015 Genetic and Evolutionary Computation Conference). 989-996. 2015. (pdf)

  • S. Jungjit and A.A. Freitas. A new genetic algorithm for multi-label correlation-based feature selection. In: Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN-2015), 285-290. 2015. (pdf)

    2014

  • R.C. Barros, M.P. Basgalupp, A.A. Freitas, A.C.P.L. de Carvalho. Evolutionary design of decision-tree algorithms tailored to microarray gene expression data sets. IEEE Transactions on Evolutionary Computation, 18(6), 873-892. Dec. 2014. (pdf)

  • G.L. Pappa, G. Ochoa, M.R. Hyde, A.A. Freitas, J. Woodward, J. Swan. Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms. Genetic Programming and Evolvable Machines, 15(1): 3-35. March 2014. (pre-print version) (pdf)

  • K.M. Salama and A.A. Freitas. Classification with cluster-based Bayesian multi-nets using Ant Colony Optimisation. Swarm and Evolutionary Computation, Vol. 18, 54-70, Oct. 2014. (pre-print version) (pdf)

  • K.M. Salama and A.A. Freitas. ABC-Miner+: constructing Markov blanket classifiers with ant colony algorithms. Memetic Computing, 6(3), 183-206, Sep. 2014. (pre-print version) (pdf)

  • F. Fabris and A.A. Freitas. Dependency network methods for hierarchical multi-label classification of gene functions. Proc. 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), 241-248. IEEE Service Center, 2014. (pdf)

  • P.B. da Silva, E.C. Goncalves, A. Plastino, A.A. Freitas. Distinct chains for different instances: an effective strategy for multi-label classifier chains. In: Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2014), Part II, Lecture Notes in Computer Science 8725, pp. 453-468. Springer. (pdf)

  • F. Fabris and A.A. Freitas. An efficient algorithm for hierarchical classification of protein and gene functions. In: Proceedings of the Twenty-Fifth International Workshop on Database and Expert System Applications (DEXA 2014), 64-68. IEEE CPS, 2014. (pdf)

  • S. Jungjit, A.A. Freitas, M. Michaelis, J. Cinatl. Extending multi-label feature selection with KEGG pathway information for microarray data analysis. In Proceedings of the 2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 8 pages. IEEE, 2013. (pdf)

  • B.C. Paes, A. Plastino, A.A. Freitas. Exploring attribute selection in hierarchical classification. Journal of Information and Data Management, Vol. 5, No. 1, Feb. 2014, pp. 124-133. (pdf)

    2013

  • A.A. Freitas. Comprehensible classification models: a position paper. ACM SIGKDD Explorations, 15(1), pp. 1-10. ACM, June 2013. (pdf)

  • C. Wan and A.A. Freitas. Prediction of the pro-longevity or anti-longevity effect of Caenorhabditis Elegans genes based on Bayesian classification methods. In: Proceedings of the 2013 IEEE International Conference on Bioinformatics and Biomedicine, pp. 373-380. IEEE, 2013. (pdf)

  • D. Newby, A.A. Freitas, T. Ghafourian. Pre-processing feature selection for improved C&RT models for oral absorption. Journal of Chemical Information Modeling 53(10), pp. 2730-2742, 2013 (pre-print version). (pdf)

  • D. Newby, A.A. Freitas, T. Ghafourian. Coping with unbalanced class data sets in oral absorption models. Journal of Chemical Information Modeling 53(2), pp. 461-474, 2013 (pre-print version). (pdf)

  • R.C. Barros, M.P. Basgalupp, A.C.P.L.F. de Carvalho, A.A. Freitas. Automatic Design of Decision-Tree Algorithms with Evolutionary Algorithms. Evolutionary Computation, Winter 2013, Vol. 21, No. 4, pp. 659-684.(pre-print version). (pdf)

  • K. Salama and A.A. Freitas. Learning Bayesian network classifiers using ant colony optimization. Swarm Intelligence, Vol. 7, Issue 2-3, pp. 229-254, Sep. 2013. (pre-print version). (pdf)

  • F.E.B. Otero, A.A. Freitas and C.G. Johnson. A new sequential covering strategy for inducing classification rules with ant colony algorithms. IEEE Transactions on Evolutionary Computation, 17(1), pp. 64-76, Feb. 2013. (pre-print, unformatted version) (pdf)

  • R.C. Barros, R. Cerri, A.A. Freitas, A.C.P.L.F. de Carvalho. Probabilistic clustering for hierarchical multi-label classification of protein functions. In: Machine Learning and Knowledge Discovery in Databases: European Conference (ECMLPKDD-2013), Proceedings, Part II – Lecture Notes in Artificial Intelligence 8189, pp. 385-400. Springer, 2013. (pdf)

  • K.M. Salama and A.A. Freitas. Extending the ABC-Miner Bayesian classification algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2013), 1-12. Springer, 2013. (pdf)

  • E.C. Goncalves, A. Plastino and A.A. Freitas A Genetic Algorithm for Optimizing the Label Ordering in Multi-Label Classifier Chains. In Proceedings of the 2013 IEEE International Conference on Tools with Artificial Intelligence (ICTAI), 469-476. (pdf)

  • L.H.C. Merschmann and A.A. Freitas. An extended local hierarchical classifier for prediction of protein and gene functions. In Proceedings of the 15th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2013), Lecture Notes in Computer Science 8057, pp. 159-171. (pdf)

  • K.M. Salama and A.A. Freitas. ACO-based Bayesian network ensembles for the hierarchical classification of ageing-related proteins. In Proceedings of the 11th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBio-2013), Lecture Notes in Computer Science 7833, pp. 80-91. Springer, 2013. (pdf) (Datasets Used in the Experiments)

  • S. Jungjit, A.A. Freitas, M. Michaelis, J. Cinatl. Two extensions to multi-lable correlation-based feature selection: a case study in bioinformatics. In Proceedings of the 2013 IEEE International Conference on Systems, Man and Cybernetics (SMC-2013), pp. 1519-1524. IEEE Computer Society Conference Publishing Services (CPS), 2013. (pdf)

  • F.E.B. Otero and A.A. Freitas. Improving the interpretability of classification rules discovered by an ant colony algorithm. In Proceedings of the 2013 Genetic and Evolutionary Computation Conference (GECCO-2013), pp. 73-80. ACM Press, 2013. (pdf)
    Note: This paper received the best conference track paper award in the Ant Colony Optimization and Swarm Intelligence track of the conference.

  • R. Cerri, R.C. Barros, A.C.P.L.F. de Carvalho, A.A. Freitas. A grammatical evolution algorithm for generation of hierarchical multi-label classification rules. In Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC-2013), pp. 454-461. IEEE Press, 2013. (pdf)
    Note: This paper received the Student Best Paper Award at this conference.

  • K.M. Salama and A.A. Freitas. Clustering-based Bayesian multi-net classifier construction with ant colony optimization. In Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC-2013), pp. 3079-3086. IEEE Press, 2013. (pdf)

  • K.M. Salama and A.A. Freitas. Investigating the impact of various classification quality measures in the predictive accuracy of ABC-Miner. To appear in Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC-2013), 2321-2328. IEEE Press, 2013. (pdf)

    2012

  • T. Ghafourian, A.A. Freitas, D. Newby. The impact of training set data distributions for modelling of passive intestinal absorption. International Journal of Pharmaceutics, 436, 711-720, 2012. (pre-print version). (pdf)

  • F.E.B. Otero, A.A. Freitas and C.G. Johnson. Inducing decision trees with an ant colony optimization algorithm. Applied Soft Computing, Vol. 12, No. 11, pp. 3615-3626, Nov. 2012. (pre-print, unformatted version) (pdf)

  • B.C. Paes, A. Plastino, A.A. Freitas. Improving local per level hierarchical classification. Journal of Information and Data Management, Vol. 3, No. 3, pp. 394-409, Oct. 2012. (pdf)

  • R.C. Barros, M.P. Basgalupp, A.C.P.L.F. de Carvalho, A.A. Freitas. A Survey of Evolutionary Algorithms for Decision Tree Induction. IEEE Transactions on Systems, Man and Cybernetics - Part C: Applications and Reviews, Vol. 42, No. 3, pp. 291-312. May 2012. (pre-print version) (pdf)

  • K.M. Salama and A.A. Freitas. ABC-Miner: an ant-based Bayesian classification algorithm. In: Swarm Intelligence: Proceedings of the 8th International Conference (ANTS 2012). Lecture Notes in Computer Science 7461, pp. 13-24. Springer, 2012. (pdf)

  • M. Medland, F.E.B. Otero, A.A. Freitas. Improving the cAnt-MinerPB classification algorithm. In: Swarm Intelligence: Proceedings of the 8th International Conference (ANTS 2012). Lecture Notes in Computer Science 7461, pp. 73-84. Springer, 2012. (pdf)

  • R.C. Barros, M.P. Basgalupp, A.C.P.L.F. de Carvalho and A.A. Freitas. A hyper-heuristic evolutionary algorithm for automatically designing decision-tree algorithms. In Proceedings of the 2012 Genetic and Evolutionary Computation Conference (GECCO'12), pp. 1237-1244. ACM Press, 2012. (pdf)
    Note: This paper received the Best Paper Award out of papers submitted to three tracks of this conference, namely the tracks: IGCE (Integrative Genetic and Evolutionary Computation) + S*S (Self-* Search) + SBSE (Search-based Software Engineering).

  • A.S.G. Meiguins, R.C. Limao, B.S. Meiguins, S.F.S. Junior and A.A. Freitas. AutoClustering: an estimation of distribution algorithm for the automatic generation of clustering algorithms. In Proceedings of WCCI 2012 - IEEE World Congress on Computational Intelligence (Congress on Evolutionary Computation), pp. 2560-2566. IEEE Press, 2012. (pdf)

  • A. Moraglio, F.E. Otero, C.G. Johnson, S. Thompson and A.A. Freitas. Evolving recursive programs using non-recursive scaffolding. In Proceedings of WCCI 2012 - IEEE World Congress on Computational Intelligence (Congress on Evolutionary Computation), pp. 1596-1603. IEEE Press, 2012. (pdf)

    2011

  • K.M. Salama, A.M. Abdelbar, A.A. Freitas. Multiple pheromone types and other extensions to the Ant-Miner classification rule discovery algorithm. Swarm Intelligence, Vol. 5, Nos. 3-4, pp. 149-182, Dec. 2011. (pre-print version) (pdf)

  • R. Cerri, A.C.P.L. F. de Carvalho and A.A. Freitas. Adapting non-hierarchical multilabel classification methods for hierarchical multilabel classification. Intelligent Data Analysis, Vol. 15, No. 6, 861-887. November 2011. (pre-print version) (pdf)

  • C.N. Silla Jr. and A.A. Freitas. Selecting different protein representations and classification algorithms in hierarchical protein function prediction. Intelligent Data Analysis, Vol. 15, No. 6, pp. 979-999. November 2011. (pre-print version) (pdf)

  • R.B. Pereira, A. Plastino, B. Zadrozny, L.H.C. Merschmann and A.A. Freitas. Lazy Attribute selection: Choosing attributes at classification time. Intelligent Data Analysis, Vol. 15, No. 5, pp. 715-732, 2011. (pre-print version) (pdf)

  • M.N. Davies, D.E. Gloriam, A. Secker, A.A. Freitas, J. Timmis and D.R. Flower. Present perspectives on the automated classification of the G-Protein Coupled Receptors (GPCRs) at the protein sequence level. Current Topics in Medical Chemistry, Vol. 11, No. 15, pp. 1994-2009, August 2011. (pre-print, unformattted version) (pdf)

  • D.F. Tsunoda, A.A. Freitas, H.S. Lopes. A genetic programming method for protein motif discovery and protein classification. Soft Computing, Vol. 15, No. 10, Oct. 2011, pp. 1897-1908. (pdf)

  • A.A. Freitas, J.P. de Magalhaes. A review and appraisal of the DNA damage theory of ageing. Mutation Research, Vol. 728, Issues 1-2, pp. 12-22, July-October 2011. (pre-print, unformatted version) (pdf)

  • A.A. Freitas, O. Vasieva, J.P. de Magalhaes. A data mining approach for classifying DNA repair genes into ageing-related or non-ageing-related. BMC Genomics, 12:27, 11 pages, 2011.
    Note: the datasets used in the experiments are available from this link

  • C.N. Silla Jr. and A.A. Freitas. A survey of hierarchical classification across different application domains. Data Mining and Knowledge Discovery, Vol. 22, No. 1-2, pp. 31-72, 2011 (pre-print version) (pdf)

  • J. Metz, A.A. Freitas, M.C. Monard, E.A. Cherman. A study on the selection of local training sets for hierarchical classification tasks. In: Proc. 2011 Brazilian National Meeting on Artificial Intelligence (ENIA-2011), pp. 572-583. Sociedade Brasileira de Computacao, 2011. (pdf)

    2010

  • F.E.B. Otero, A.A. Freitas, C.G. Johnson. A hierarchical multi-label classification ant colony algorithm for protein function prediction. Memetic Computing, Vol. 2, No. 3, Sep 2010, pp. 165-181. (pre-print version) (pdf)

  • A.A. Freitas, D.C. Wieser, R. Apweiler. On the importance of comprehensible classification models for protein function prediction. IEEE/ACM Trans. on Computational Biology and Bioinformatics, Vol. 7, No. 1, pp. 172-182, Jan.-Mar. 2010 (pre-print version) (pdf)

  • A. Secker, M.N. Davies, A.A. Freitas, E. Clark, J. Timmis and D.R. Flower. Hierarchical classification of G-Protein-Coupled Receptors with data-driven selection of attributes and classifiers. Int. J. Data Mining and Bioinformatics, Vol. 4, No. 2, 2010, pp. 191-210. (pre-print) (pdf)

  • R.C. Barros, M.P. Basgalupp, D.D. Ruiz, A.C.P.L.F. de Carvalho, A.A. Freitas. Evolutionary model tree induction. In: Proc. 25th Annual ACM Symposium on Applied Computing (SAC-2010), pp. 1131-1137. ACM Press, 2010. (pdf)

  • R.T. Alves, M.R. Delgado and A.A. Freitas. Knowledge discovery with artificial immune systems for hierarchical multi-label classification of protein functions. In: Proc. 2010 World Congress on Computational Intelligence (WCCI-2010/FUZZ-IEEE-2010), pp. 2098-2105. (pdf)

  • J.C. Xavier, A.A. Freitas, A.M.P. Canuto, L.M.G. Goncalves. Web log data clustering for a multi-agent recommendation system. In: Proc. 2010 Int. Conf. on Machine Learning and Cybernetics (ICMLC), pp. 471-476. (pdf)
  • G.L. Pappa and A.A. Freitas. Creating rule ensembles from automatically-evolved rule induction algorithms. Advances in Machine Learning I: Dedicated to the memory of Professor Ryszard S. Michalski. Series Studies on Computational Intelligence, Vol. 262, pp. 257-273. Springer. 2010. (pre-print version) (pdf)

    2009

  • G.L. Pappa and A.A. Freitas. Automatically evolving rule induction algorithms tailored to the prediction of postsynaptic activity in proteins. Intelligent Data Analysis, Vol. 13, No. 2, 2009, pp. 243-259. (pre-print, unformatted version) (pdf)

  • G.L. Pappa and A.A. Freitas. Evolving rule induction algorithms with multi-objective grammar-based genetic programming. Knowledge and Information Systems, Vol. 19, No. 3, June 2009, pp. 283-309 (pre-print, unformatted version) (pdf)

  • E.R. Hruschka, R.J.G.B. Campello, A.A. Freitas and A.C.P.L.F. de Carvalho. A survey of evolutionary algorithms for clustering. IEEE Transactions on Systems, Man and Cybernetics - Part C: Applications and Reviews. Vol. 39, No. 2, March 2009, pp. 133-155. (pre-print version) (pdf)

  • N. Holden and A.A. Freitas. Hierarchical classification of protein function with ensembles of rules and particle swarm optimisation. Soft Computing journal, Vol. 13, No. 3, Feb. 2009, pp. 259-272. (pre-print version) (pdf) (the datasets used in the experiments are available from here)

  • C.N. Silla Jr. and A.A. Freitas. A global-model naive Bayes approach to the hierarchical prediction of protein functions. In: Proc. Ninth IEEE Int. Conf. on Data Mining (ICDM-2009), pp. 992-997. IEEE Press, 2009. (pdf)

  • C.N. Silla Jr. and A.A. Freitas. Novel top-down approaches for hierarchical classification and their application to automatic music genre classification. In: Proc. 2009 IEEE Int. Conf. on Systems, Man and Cybernetics (SMC-2009), pp. 3499-3504. IEEE Press, 2009. (pdf)

  • F.E.B. Otero, A.A. Freitas and C.G. Johnson. A hierarchical classification ant colony algorithm for predicting gene ontology terms. In Proc. 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBio-2009), Lecture Notes in Computer Science 5483, pp. 68-79. (pdf)

  • F.E.B. Otero and A.A. Freitas and C.G. Johnson. Handling continuous attributes in ant colony classification algorithms. In Proc. 2009 IEEE Symposium on Computational Intelligence in Data Mining (CIDM 2009), pp. 225-231. (pdf)

  • A. Plastino, E.R. Fonseca, R. Fuchshuber, S.L. Martins, A.A. Freitas, M. Luis, S. Salhi. A hybrid data mining metaheuristic for the p-median problem. In Proc. Ninth SIAM Int. Conf. on Data Mining (SDM-2009), pp. 305-316. (pdf)

  • M.P. Basgalupp, R.C. Barros, A.C.P.L.F. de Carvalho, A.A. Freitas and D.D. Ruiz. LEGAL-Tree: a lexicographical multi-objective genetic algorithm for decision tree induction. Proc. 2009 ACM Symposium on Applied Computing (SAC-2009), pp. 1085-1090. (pdf)

  • A.C.P.L.F. de Carvalho and A.A. Freitas. A tutorial on multi-label classification techniques. Foundations of Computational Intelligence, Vol 5., Studies in Computational Intelligence 205, pp. 177-195. Springer, 2009. (pre-print version) (pdf)

  • M. Iqbal, A.A. Freitas, C.G. Johnson. A hybrid rule-induction/likelihood-ratio based approach for predicting protein-protein interactions. In: C.L. Mumford and L.C. Jain (Eds.) Computational Intelligence: collaboration, fusion and emergence, pp. 623-637. Springer, 2009. (pre-print version) (pdf)

  • F. Otero, M. Segond, A.A. Freitas, C.G. Johnson, D. Robilliard, C. Fonlupt. An empirical evaluation of the effectiveness of different types of predictor attributes in protein function prediction. In: A. Abraham, A.-E. Hassanien, V. Snael (Eds.) Foundations of Computational Intelligence, Vol 5, Studies in Computational Intelligence 205, pp. 339-357. Springer, 2009. (pre-print version) (pdf)

    2008

  • M.N. Davies, A. Secker, A.A. Freitas, J. Timmis, E. Clark, D.R. Flower. Alignment-independent techniques for protein classification. Current Proteomics, Vol. 5, No. 4, Dec. 2008, pp. 217-223. (pre-print, unformatted version) (pdf)

  • M.N. Davies, A. Secker, A.A. Freitas, E. Clark, J. Timmis, D.R. Flower. Optimizing amino acid groupings for GPCR classification. Bioinformatics Vol. 24, No. 18, 2008, pp. 1980-1986. (pre-print version) (pdf)

  • M. Iqbal, A.A. Freitas, C.G. Johnson, M. Vergassola. Message-passing algorithms for the prediction of protein domain interactions from protein-protein interaction data. Bioinformatics Vol. 24, No. 18, 2008, pp. 2064-2070. (pre-print version) (pdf)

  • N. Holden and A.A. Freitas. A hybrid PSO/ACO algorithm for discovering classification rules in data mining. Journal of Artificial Evolution and Applications (JAEA), special issue on Particle Swarms: The Second Decade, Vol. 2008, Article Id 316145, 11 pages. (pdf)

  • E.S. Correa, A.A. Freitas and C.G. Johnson. Particle swarm for attribute selection in Bayesian classification: an application to protein function prediction. Journal of Artificial Evolution and Applications (JAEA), special issue on Particle Swarms: The Second Decade, Vol. 2008, Article Id 876746, 12 pages. (pdf)

  • A. Secker, A.A. Freitas and J. Timmis. AISIID: an artificial immune system for interesting information discovery on the web. Applied Soft Computing 8 (2008), pp. 885-905. (pdf)

  • F.E.B. Otero, A.A. Freitas and C.G. Johnson. cAnt-Miner: an ant colony classification algorithm to cope with continuous attributes. In: Ant Colony Optimization and Swarm Intelligence (Proc. ANTS-2008), Lecture Notes in Computer Science 5217, pp. 48-59. Springer, 2008. (pdf)

  • E.P. Costa, A.C. Lorena, A.C.P.L.F. de Carvalho, A.A. Freitas. Top-down hierarchical ensembles of classifiers for predicting G-protein-coupled-receptor functions. In: Advances in Bioinformatics and Computational Biology (Proc. BSB-2008), Lecture Notes in Bioinformatics 5167, pp. 35-46. Springer, 2008. (pdf)

  • R.T. Alves, M.R. Delgado, A.A. Freitas. Multi-label hierarchical classification of protein functions with artificial immune systems. In: Advances in Bioinformatics and Computational Biology (Proc. BSB-2008), Lecture Notes in Bioinformatics 5167, pp. 1-12. Springer, 2008. (pdf)

  • A. Secker, M.N. Davies, A.A. Freitas, J. Timmis, E. Clark, D.R. Flower. An artificial immune system for evolving amino acid clusters tailored to protein function prediction. In Proc. 2008 Int. Conf. on Artificial Immune Systems (ICARIS-2008), Lecture Notes in Computer Science 5132, pp. 242-253. Springer, 2008. (pdf)

  • N. Holden and A.A. Freitas. Improving the performance of hierarchical classification with swarm intelligence. In Proc. 6th European Conf. on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBio-2008). Lecture Notes in Computer Science 4973, pp. 48-60. Springer, 2008. (pdf)
    Note: This paper received the Best Paper Award at this conference.

  • M. Iqbal, A.A. Freitas and C.G. Johnson. Protein interaction inference using particle swarm optimization algorithm. In Proc. 6th European Conf. on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBio-2008). Lecture Notes in Computer Science 4973, pp. 61-70. Springer, 2008. (pdf)

  • A.A. Freitas, R.S. Parpinelli, H.S. Lopes. Ant Colony Algorithms for Data Classification. In: M. Khosrou-Pour (Ed.) Encyclopedia of Information Science and Technology, 2nd Ed, pp. 154-159. Information Science Reference, 2008. (pre-print version) (pdf)

    2007

  • M.N. Davies, A. Secker, A.A. Freitas, M. Mendao, J. Timmis and D.R. Flower. On the hierarchical classification of G protein-coupled-receptors. Bioinformatics 2007, Vol. 23, No. 23, 1 December 2007, pp. 3113-3118. (pre-print version) (pdf)

  • M.N. Davies, D.E. Gloriam, A. Secker, A.A. Freitas, M. Mendao, J. Timmis and D.R. Flower. Proteomics applications of automated GPCR classification. Proteomics 7, 2007, pp. 2800-2814. (pdf)

  • A.A. Freitas and J. Timmis. Revisiting the Foundations of Artificial Immune Systems for Data Mining. IEEE Trans. on Evolutionary Computation, Vol. 11, Issue 4, pp. 521-540, Aug. 2007. (pre-print, unformatted version) (pdf)

  • A. Secker, M.N. Davies, A.A. Freitas, J. Timmis, M. Mendao, D. Flower. An experimental comparison of classification algorithms for the hierarchical prediction of protein function. Expert Update (the BCS-SGAI Magazine), Vol. 9, No. 3, Special Issue on the 3rd UK KDD Workshop, pp. 17-22, Autumn 2007. (pdf)

  • E.P. Costa, A.C. Lorena, A.C.P.L.F. Carvalho, A.A. Freitas and N. Holden. Comparing several approaches for hierarchical classification of proteins with decision trees. Advances in Bioinformatics and Computational Biology (Proc. Second Brazilian Symposium on Bioinformatics, BSB-2007), LNBI 4643, pp. 126-137. Springer, 2007. (pdf)

  • E.P. Costa, A.C. Lorena, A.C.P.L.F. Carvalho, and A.A. Freitas. A review of performance evaluation measures for hierarchical classifiers. In: Evaluation Methods for Machine Learning II: papers from the 2007 AAAI Workshop, pp. 1-6. Vancouver, AAAI Press, 2007. (pdf)

  • E.S. Correa, A.A. Freitas and C.G. Johnson. Particle swarm and bayesian networks applied to attribute selection for protein functional classification. In Proc. of the GECCO-2007 Workshop on Particle Swarms: The Second Decade, pp. 2651-2658. ACM Press, 2007. (pdf)

  • N. Holden and A.A. Freitas. A hybrid PSO/ACO algorithm for classification. In Proc. of the GECCO-2007 Workshop on Particle Swarms: The Second Decade, pp. 2745-2750. ACM Press, 2007. (pdf)

  • A. Secker and A.A. Freitas. WAIRS: Improving classification accuracy by weighting attributes in the AIRS classifier. To appear in 2007 Congress on Evolutionary Computation (CEC-2007), Singapore, 2007. (pdf)

  • A.A. Freitas. A Review of Evolutionary Algorithms for Data Mining. In: O. Maimon and L. Rokach (Eds.) Soft Computing for Knowledge Discovery and Data Mining, pp. 61-93. Springer, 2007. (pre-print version) (pdf)

  • A.A. Freitas and A.C.P.L.F. de Carvalho. A Tutorial on Hierarchical Classification with Applications in Bioinformatics. In: D. Taniar (Ed.) Research and Trends in Data Mining Technologies and Applications, pp. 175-208. Idea Group, 2007. (pre-print, unformatted version) (pdf)

  • A.A. Freitas, K. McGarry and E.S. Correa. Integrating Bayesian networks and Simpson's paradox in data mining. In: F. Russo and J. Williamson (Eds.) Causality and Probability in the Sciences, pp. 43-62. London: College Publications, 2007. (pre-print, unformatted version) (pdf)

    2006

  • A.A. Freitas. Are we really discovering "interesting" knowledge from data? Expert Update (the BCS-SGAI Magazine), Vol. 9, No. 1, Special Issue on the 2nd UK KDD Workshop, pp. 41-47, Autumn 2006. (pre-print, unformatted version) (pdf)

  • C.C. Fabris and A.A. Freitas. Discovering surprising instances of Simpson's paradox in hierarchical multidimensional data. Int. Journal of Data Warehousing and Mining, 2(1), pp. 26-48, Jan-Mar 2006. (pre-print version) (pdf)

  • G.L. Pappa and A.A. Freitas. Automatically evolving rule induction algorithms. In: Proc. ECML-2006 (17th European Conf. on Machine Learning), LNAI 4212, pp. 341-352. Springer, 2006. (pdf)

  • N. Miles, A.A. Freitas and S. Serjeant. Estimating photometric redshifts using genetic algorithms. In: Applications and Innovations in Intelligent Systems XIV - Proc. of AI-2006, pp. 75-87. Springer, 2006. (pdf)

  • J. Smaldon and A.A. Freitas. Improving the interpretability of classification rules in sparse bioinformatics datasets. In: Research and Development in Intelligent Systems XXIII - Proc. of AI-2006, pp. 377-381. Springer, 2006. (pdf)

  • A. Chan and A.A. Freitas. A new ant colony algorithm for multi-label classification with applications in bioinformatics. In: Proc. Genetic and Evolutionary Computation Conference (GECCO-2006), pp. 27-34. ACM, 2006. (pdf)

  • E.S. Correa, A.A. Freitas and C.G. Johnson. A new discrete particle swarm algorithm applied to attribute selection in a bioinformatics data set. In: Proc. Genetic and Evolutionary Computation Conference (GECCO-2006), pp. 35-42. ACM, 2006. (pdf)

  • J. Smaldon and A.A. Freitas. A new version of the Ant-Miner algorithm discovering unordered rule sets. In: Proc. Genetic and Evolutionary Computation Conference (GECCO-2006), pp. 43-50. ACM, 2006. (pdf)

  • N. Holden and A.A. Freitas. Hierarchical Classification of G-Protein-Coupled Receptors with a PSO/ACO Algorithm. In: Proc. IEEE Swarm Intelligence Symposium (SIS-06), pp. 77-84. IEEE, 2006. (pdf)

    2005

  • G.L. Pappa, A.J. Baines and A.A. Freitas. Predicting post-synaptic activity in proteins with data mining. Bioinformatics Vol. 21 Suppl. 2, 2005, pp. ii19-ii25. (pre-print version) (pdf), (dataset used in the experiments)

  • D.R. Carvalho and A.A. Freitas. Evaluating Six Candidate Solutions for the Small-Disjunct Problem and Choosing the Best Solution via Meta Learning. Artificial Intelligence Review, 24(1), pp. 61-98, Sep. 2005. (pre-print version) (pdf)

  • A. Chan and A.A. Freitas. A New Classification-Rule Pruning Procedure for an Ant Colony Algorithm. Artificial Evolution (Proc. EA-2005). LNAI 3871, pp. 25-36. Springer, 2005. (pdf)

  • D.R. Carvalho, A.A. Freitas and N. Ebecken. Evaluating the correlation between objective rule interestingness measures and real human interest. Proc. European Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD-2005). LNAI 3721, pp. 453-461. Springer, 2005. (pdf)

  • N. Holden and A.A. Freitas. A hybrid particle swarm/ant colony algorithm for the classification of hierarchical biological data. Proc. 2005 IEEE Swarm Intelligence Symposium, pp. 100-107. IEEE, 2005. (pdf)

  • D.F. Tsunoda, H.S. Lopes and A.A. Freitas. An evolutionary approach for motif discovery and transmembrane protein classification. Applications of Evolutionary Computing (Proc. of EvoBIO-2005: 3rd European Workshop on Evolutionary Bioinformatics), Lecture Notes in Computer Science 3449, pp. 105-114, Springer, 2005. (pdf)

  • A. Secker, A.A. Freitas, J. Timmis. Towards a Danger Theory Inspired Artificial Immune System for Web Mining. In: A. Scime (Ed.) Web Mining: applications and techniques, pp. 145-168. Idea Group, 2005. (pre-print, unformatted version) (pdf)

    2004

  • A.A. Freitas. A Critical Review of Multi-Objective Optimization in Data Mining: a position paper. ACM SIGKDD Explorations, 6(2), pp. 77-86, 2004. (pre-print version) (pdf)

  • D.R. Carvalho and A.A. Freitas. A hybrid decision tree/genetic algorithm method for data mining. Information Sciences 163(1-3), pp. 13-35. June 2004. (pre-print, unformatted version) (pdf)

  • W. Romao, A.A. Freitas, I.M.S. Gimenes. Discovering Interesting Knowledge from a Science & Technology Database with a Genetic Algorithm. Applied Soft Computing 4(2004), pp. 121-137. (pre-print, unformatted version) (pdf)

  • C.C. Bojarczuk, H.S. Lopes, A.A. Freitas, E.L. Michalkiewicz. A constrained-syntax genetic programming system for discovering classification rules: application to medical data sets. AI in Medicine 30(2004), pp. 27-48. (pre-print, unformatted version) (pdf)

  • G.L. Pappa and A.A. Freitas. Towards a genetic programming algorithm for automatically evolving rule induction algorithms. Proc. ECML/PKDD-2004 Workshop on Advances in Inductive Rule Learning, 93-108. Pisa, Italy, Sep. 2004. (pdf)

  • C.N. Silla Jr., G.L. Pappa, A.A. Freitas, C.A.A. Kaestner. Automatic text summarization with genetic algorithm-based attribute selection. Advances in Artificial Intelligence (Proc. IX Ibero-American Conf. on Artificial Intelligence - IBERAMIA-2004), LNCS 3315, pp. 305-314, Springer, 2004. (pdf)

  • S.N.M. Ferreira, A.A. Freitas and B.C. Avila. Handling inconsistency in distributed data mining with paraconsistent logic. Proc. 13th Turkish Symp. on Artificial Intelligence and Neural Networks, 19-28. Izmir, Turkey, June 2004. (pdf)

  • R.T. Alves, M.R. Delgado, H.S. Lopes and A.A. Freitas. An artificial immune system for fuzzy-rule induction in data mining. Proc. Parallel Problem Solving from Nature (PPSN-2004), LNCS 3242, pp. 1011-1020, Springer 2004. (pdf)

  • N. Holden and A.A. Freitas. Web page classification with an ant colony algorithm. Proc. Parallel Problem Solving from Nature (PPSN-2004), LNCS 3242, pp. 1092-1102. Springer, 2004. (pdf)

  • G.L. Pappa, A.A. Freitas and C.A.A. Kaestner. Multi-Objective Algorithms for Attribute Selection in Data Mining. In: C.A. Coello Coello and G.B. Lamont (Eds.) Applications of Multi-Objective Evolutionary Algorithms, pp. 603-626. World Scientific, 2004. (pre-print, unformatted version) (pdf)

    2003

  • A. Secker, A.A. Freitas and J. Timmis. AISEC: an artificial immune system for e-mail classification. Proc. of the Congress on Evolutionary Computation (CEC-2003), pp. 131-139, Canberra. Australia, December 2003. IEEE Press, 2003. (pdf)

  • D.R. Carvalho, A.A. Freitas, N.F.F. Ebecken. A critical review of rule surprisingness measures. Proc. Data Mining IV - Int. Conf. on Data Mining, pp.545-556, Rio de Janeiro, Brazil, Dec. 2003. WIT Press, 2003. (pdf)

  • C.N. Silla Jr., C.A.A. Kaestner, A.A. Freitas. A non-linear topic detection method for text summarization using Wordnet. Proc. 1st Workshop on Information Technology and Human Language. Sao Carlos - SP, Brazil: ICMC-USP, 2003. (pdf)

  • A.A. Freitas and J. Timmis. Revisiting the foundations of artificial immune systems: a problem-oriented perspective. Artificial Immune Systems: Proc. 2nd Int. Conf. (ICARIS-2003), Lecture Notes in Computer Science 2787, pp. 229-241. Springer-Verlag, 2003. (ps)

  • A. Secker, A.A. Freitas and J. Timmis. A danger theory inspired approach to web mining. Artificial Immune Systems: Proc. 2nd Int. Conf. (ICARIS-2003), Lecture Notes in Computer Science 2787, pp. 156-167. Springer-Verlag, 2003. (pdf)

  • F.E.B. Otero, M.M.S. Silva, A.A. Freitas and J.C. Nievola. Genetic Programming for Attribute Construction in Data Mining. Genetic Programming: Proc. 6th European Conference (EuroGP-2003). Lecture Notes in Computer Science 2610, pp. 384-393. Springer, 2003. (pdf)

  • C.C. Bojarczuk, H.S. Lopes and A.A. Freitas. An innovative application of a constrained-syntax genetic programming system to the problem of predicting survival of patients. Genetic Programming: Proc. 6th European Conference (EuroGP-2003). Lecture Notes in Computer Science 2610, pp. 11-21. Springer, 2003. (pdf)

    2002

  • D.R. Carvalho and A.A. Freitas. A genetic algorithm for discovering small disjunct rules in data mining. Applied Soft Computing, 2(2), pp. 75-88, Dec. 2002. (pre-print, unformatted version) (pdf)

  • R.S. Parpinelli, H.S. Lopes and A.A. Freitas. Data Mining with an Ant Colony Optimization Algorithm. IEEE Trans. on Evolutionary Computation, special issue on Ant Colony algorithms, 6(4), pp. 321-332, Aug. 2002. (pre-print, unformatted version) (pdf)

  • E. Noda, A.L.V. Coelho, I.L.M. Ricarte, A. Yamakami and A.A. Freitas. Devising adaptive migration policies for cooperative distributed genetic algorithms. Proc. 2002 IEEE Int. Conf. on Systems, Man and Cybernetics (SMC-2002). (Published in CD-ROM.) IEEE Press, 2002. (pdf)

  • G.L. Pappa, A.A. Freitas and C.A.A. Kaestner. A multiobjective genetic algorithm for attribute selection. Proc. 4th Int. Conf. on Recent Advances in Soft Computing (RASC-2002), pp. 116-121. Published in CD-ROM (ISBN: 1-84233-0764). Nottingham Trent University, Nottingham, UK. Dec. 2002. (pdf)

  • O. Larsen, A.A. Freitas and J.C. Nievola. Constructing X-of-N attributes with a genetic algorithm. Proc. 4th Int. Conf. on Recent Advances in Soft Computing (RASC-2002), pp. 326-331. Published in CD-ROM (ISBN: 1-84233-0764). Nottingham Trent University, Notthingham, UK. Dec. 2002. (pdf)

  • D.R. Carvalho and A.A. Freitas. New results for a hybrid decision tree/genetic algorithm for data mining. Proc. 4th Int. Conf. on Recent Advances in Soft Computing (RASC-2002), pp. 260-265. Published in CD-ROM (ISBN: 1-84233-0764), Nottingham Trent University, Notthingham, UK. Dec. 2002. (pdf)

  • G.L. Pappa, A.A. Freitas and C.A.A. Kaestner. Attribute Selection with a Multiobjective Genetic Algorithm. Proc. 16th Brazilian Symposium on Artificial Intelligence (SBIA-2002) - Lecture Notes in Artificial Intelligence 2507, pp. 280-290. Springer-Verlag, 2002. (postscript)

  • J. Larocca Neto, A.A. Freitas and C.A.A. Kaestner. Automatic Text Summarization using a Machine Learning Approach. Proc. 16th Brazilian Symposium on Artificial Intelligence (SBIA-2002) - Lecture Notes in Artificial Intelligence 2507, pp. 205-215. Springer-Verlag, 2002. (pdf)

  • E. Noda, A.A. Freitas and A. Yamakami. A distributed-population genetic algorithm for discovering interesting prediction rules. 7th Online World Conference on Soft Computing (WSC7). Held on the Internet, Sep. 2002. (pdf)

  • W. Romao, A.A. Freitas and R.C.S. Pacheco. A Genetic Algorithm for Discovering Interesting Fuzzy Prediction Rules: applications to science and technology data. Proc. Genetic and Evolutionary Computation Conf. (GECCO-2002), pp. 1188-1195. New York, July 2002. (pdf)

  • D.R. Carvalho and A.A. Freitas. A genetic algorithm with sequential niching for discovering small-disjunct rules. Proc. Genetic and Evolutionary Computation Conf. (GECCO-2002), pp. 1035-1042. New York, July 2002. (pdf)

  • A.A. Freitas. A Review of Evolutionary Algorithms for E-Commerce. In: J. Segovia, P.S. Szczepaniak, M. Niedzwiedzinski (Eds.) E-Commerce and Intelligent Methods. Studies in Fuzziness and Soft Computing, Vol. 105, pp. 159-179. Heidelberg: Springer-Verlag, 2002. (pre-print, unformatted version) (postscript) (pdf)

  • A.A. Freitas. Evolutionary Computation. W. Klosgen and J. Zytkow (Eds.) Handbook of Data Mining and Knowledge Discovery, pp. 698-706. Oxford University Press, 2002. (pre-print, unformatted version) (postscript) (pdf)

  • R.S. Parpinelli, H.S. Lopes and A.A. Freitas. An Ant Colony Algorithm for Classification Rule Discovery. In: H. Abbass, R. Sarker, C. Newton. (Eds.) Data Mining: a Heuristic Approach, pp. 191-208. London: Idea Group Publishing, 2002. (pre-print, unformatted version) (pdf)

    2001

  • A.A. Freitas. Understanding the Crucial Role of Attribute Interaction in Data Mining. Artificial Intelligence Review 16(3), Nov. 2001, pp. 177-199. (pre-print, unformatted version) (postscript) (pdf)

  • R.R.F. Mendes, F.B. Voznika, A.A. Freitas and J.C. Nievola. Discovering fuzzy classification rules with genetic programming and co-evolution. Principles of Data Mining and Knowledge Discovery (Proc. 5th European Conf., PKDD 2001) - Lecture Notes in Artificial Intelligence 2168, pp. 314-325. Springer-Verlag, 2001. (postscript)

  • C.E. Bojarczuk, H.S. Lopes and A.A. Freitas. Data mining with constrained-syntax genetic programming: applications in medical data sets. Proc. Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-2001), a Workshop at Medinfo-2001. London, UK, Sep. 2001. (postscript)

  • C.C. Fabris and A.A. Freitas. Incorporating deviation-detection functionality into the OLAP paradigm. Proc. XVI Brazilian Symp. on Databases (SBBD-2001), pp. 274-285. Rio de Janeiro, Brazil. October 2001. (postscript)

  • R.S. Parpinelli, H.S. Lopes and A.A. Freitas. An ant colony based system for data mining: applications to medical data. Proc. 2001 Genetic and Evolutionary Computation Conf. (GECCO-2001), pp. 791-798. Morgan Kaufmann, 2001. (postscript)

  • E.S. Correa, M.T.A. Steiner, A.A. Freitas and C. Carnieri. A genetic algorithm for the P-median problem. Proc. 2001 Genetic and Evolutionary Computation Conf. (GECCO-2001), pp. 1268-1275. Morgan Kaufmann, 2001. (postscript)

  • D.R. Carvalho and A.A. Freitas. An immunological algorithm for discovering small-disjunct rules in data mining. Proc. Graduate Student Workshop at GECCO-2001, pp. 401-404. San Francisco, CA, USA. July 2001. (postscript)

    2000

  • C.C. Bojarczuk, H.S. Lopes, A.A. Freitas. Genetic programming for knowledge discovery in chest pain diagnosis. IEEE Engineering in Medicine and Biology magazine - special issue on data mining and knowledge discovery, 19(4), 38-44, July/Aug. 2000. (pre-print, unformatted version) (postscript) (pdf)

  • W. Romao, A.A. Freitas and R.S. Pacheco. Uma revisao de abordagens genetico-difusas para descoberta de conhecimento em banco de dados. (In Portuguese) Acta Scientiarum 22(5), 1347-1359. Dec. 2000. Universidade Estadual de Maringa, Brazil. (pre-print, unformatted version) (postscript) (pdf)

  • A.A. Freitas. Understanding the crucial differences between classification and discovery of association rules - a position paper. ACM SIGKDD Explorations, 2(1), 65-69. ACM, 2000. (postscript) (pdf)

  • J. Larocca Neto, A.D. Santos, C.A.A. Kaestner, A.A. Freitas. Generating Text Summaries through the Relative Importance of Topics. Proc. Int. Joint Conf.: IBERAMIA-2000 (7th Ibero-American Conf. on Artif. Intel.) & SBIA-2000 (15th Brazilian Symp. on Artif. Intel.) Lecture Notes in Artificial Intelligence 1952, pp. 301-309. Sao Paulo, SP, Brazil. Nov. 2000. (postscript)

  • J. Larocca Neto, A.D. Santos, C.A.A. Kaestner, A.A. Freitas, J.C. Nievola. A trainable algorithm for summarizing news stories. Proc. PKDD-2000 Workshop on Machine Learning and Textual Information Access. Lyon, France. Sep. 2000. (postscript)

  • D.R. Carvalho and A.A. Freitas. A genetic algorithm-based solution for the problem of small disjuncts. Principles of Data Mining and Knowledge Discovery (Proc. 4th European Conf., PKDD-2000. Lyon, France). Lecture Notes in Artificial Intelligence 1910, 345-352. Springer-Verlag, 2000. (postscript)

  • D.R. Carvalho and A.A. Freitas. A hybrid decision tree/genetic algorithm for coping with the problem of small disjuncts in data mining. Proc. 2000 Genetic and Evolutionary Computation Conf. (GECCO-2000), 1061-1068. Las Vegas, NV, USA. July 2000. (postscript)

  • D.L.A. Araujo, H.S. Lopes and A.A. Freitas. Rule discovery with a parallel genetic algorithm. Proc. 2000 Genetic and Evolutionary Computation (GECCO-2000) Workshop Program, 89-92. Las Vegas, NV, USA. July 2000. (postscript)

  • M.V. Fidelis, H.S. Lopes and A.A. Freitas. Discovering comprehensible classification rules with a genetic algorithm. Proc. Congress on Evolutionary Computation - 2000 (CEC-2000), 805-810. La Jolla, CA, USA, July/2000. (postscript)

  • J. Larocca Neto, A.D. Santos, C.A.A. Kaestner, A.A. Freitas. The integrated data mining tool MineKit and a case study of its application on video shop data. Proc. 2nd Int. ICSC Symp. on Engineering of Intelligent Systems (EIS-2000). Scotland, July 2000. ICSC Academic Press. (Published in CD-ROM, ISBN: 3-906454-21-5) (postscript)

  • R. Santos, J.C. Nievola and A.A. Freitas. Extracting comprehensible rules from neural networks via genetic algorithms. Proc. 2000 IEEE Symp. on Combinations of Evolutionary Computation and Neural Networks (ECNN-2000), 130-139. San Antonio, TX, USA. May 2000. (postscript)

  • J. Larocca Neto, A.D. Santos, C.A.A. Kaestner, A.A. Freitas. Document clustering and text summarization. Proc. 4th Int. Conf. Practical Applications of Knowledge Discovery and Data Mining (PADD-2000), 41-55. London: The Practical Application Company. 2000. (postscript)

    1999

  • A.A. Freitas. On rule interestingness measures. Knowledge-Based Systems journal 12 (5-6), 309-315. Oct. 1999. (pre-print, unformatted version) (postscript) (pdf)

  • S. Lavington, N. Dewhurst, E. Wilkins and A. Freitas. Interfacing knowledge discovery algorithms to large database management systems. Information and Software Technology journal - special issue on Knowledge Discovery and Data Mining, 41(9), 605-617. June 1999. (to get a paper copy, contact me )

  • C.C. Fabris and A.A. Freitas. Discovering surprising patterns by detecting occurrences of Simpson's paradox. In: Research and Development in Intelligent Systems XVI (Proc. ES99, The 19th SGES Int. Conf. on Knowledge-Based Systems and Applied Artificial Intelligence), 148-160. Springer-Verlag, 1999. (postscript)

  • D.L.A. Araujo, H.S. Lopes, A.A. Freitas. A parallel genetic algorithm for rule discovery in large databases. Proc. 1999 IEEE Systems, Man and Cybernetics Conf., v. III, 940-945. Tokyo, Oct. 1999. (postscript)

  • C.S. Fertig, A.A. Freitas, L.V.R. Arruda and C. Kaestner. A Fuzzy Beam-Search Rule Induction Algorithm. Principles of Data Mining and Knowledge Discovery: Proc. 3rd European Conf. (PKDD-99) Lecture Notes in Artificial Intelligence 1704, 341-347. Springer-Verlag, 1999. (postscript)

  • E. Noda, A.A. Freitas, H.S. Lopes. Discovering interesting prediction rules with a genetic algorithm. Proc. Congress on Evolutionary Computation (CEC-99), 1322-1329. Washington D.C., USA, July 1999. (postscript)

  • C.E. Bojarczuk, H.S. Lopes and A.A. Freitas. Discovering comprehensible classification rules using genetic programming: a case study in a medical domain. Proc. Genetic and Evolutionary Computation Conference (GECCO-99) 953-958. Orlando, FL, USA, July 1999. (postscript)

  • A.A. Freitas. A Summary of the Papers Presented at the AAAI-99 & GECCO-99 Workshop on Data Mining with Evolutionary Algorithms: Research Directions. (1-page extended abstract). Proc. of the GECCO-99, Workshop Program, 226. Orlando, FL, USA. July 1999. (postscript)

  • D.R. Carvalho, B.C. Avila, A.A. Freitas. A hybrid genetic algorithm / decision tree approach for coping with unbalanced classes. Proc. 3rd Int. Conf. on the Practical Applications of Knowledge Discovery & Data Mining (PADD-99), 61-70. Londres, April 1999. (postscript)

    1998

  • A.A. Freitas. A genetic algorithm for generalized rule induction. In: R. Roy et al. Advances in Soft Computing - Engineering Design and Manufacturing, 340-353. (Proc. WSC3, 3rd On-Line World Conference on Soft Computing, hosted on the Internet, July 1998.) Springer-Verlag, 1999. (postscript)

  • A.A. Freitas. On objective measures of rule surprisingness. Principles of Data Mining & Knowledge Discovery (Proc. 2nd European Symp., PKDD'98. Nantes, France, Sep. 1998). Lecture Notes in Artificial Intelligence 1510, 1-9. Springer-Verlag, 1998. (postscript)

  • A.A. Freitas. A multi-criteria approach for the evaluation of rule interestingness. Data Mining. (Proc. Int. Conf., Rio de Janeiro, Brazil, Sep. 1998), 7-20. WIT Press, 1998. (postscript)

  • A.A. Freitas. A Survey of Parallel Data Mining. Proc. 2nd Int. Conf. on the Practical Applications of Knowledge Discovery and Data Mining, 287-300. London: The Practical Application Company, Mar. 1998. (postscript)

    1997

  • A.A. Freitas. A genetic programming framework for two data mining tasks: classification and generalized rule induction. Genetic Programming 1997: Proc. 2nd Annual Conf. (Stanford University, July 1997), 96-101. Morgan Kaufmann, 1997. (postscript)

  • A.A. Freitas. Towards large-scale knowledge discovery in databases (KDD) by exploiting parallelism in generic KDD primitives. Proc. 3rd Int. Workshop on Next-Generation Info. Technologies and Systems, 33-43. Neve Ilan, Israel, July 1997. (postscript)

  • A.A. Freitas. The principle of transformation between efficiency and effectiveness: towards a fair evaluation of the cost-effectiveness of KDD techniques. Principles of Data Mining and Knowledge Discovery (Proc. 1st European Symp. Trondheim, Norway. June 1997). Lecture Notes in Artificial Intelligence 1263, 299-306. Springer-Verlag, 1997. (postscript)

    1996

  • A.A. Freitas & S.H. Lavington. A framework for data-parallel knowledge discovery in databases. (Extended Abstract) IEE Colloquium on Knowledge Discovery and Data Mining. Digest No. 96/198, pp.6/1-6/4. London: IEE, Oct./96 (postscript)

  • A.A. Freitas & S.H. Lavington. Speeding up knowledge discovery in large relational databases by means of a new discretization algorithm. In: R. Morrison & J. Kennedy. (Ed.) LNCS 1094: Advances in Databases (Proc. 14th British Nat. Conf. on Databases - BNCOD-14, Edinburgh, UK, July/96), 124-133. Springer-Verlag, 1996. (postscript)

  • A.A. Freitas & S.H. Lavington. Using SQL primitives and parallel DB servers to speed up knowledge discovery in large relational databases. In: R. Trappl. (Ed.) Cybernetics and Systems'96: Proc. 13th European Meeting on Cybernetics and Systems Research, 955-960. Vienna, Apr./96 (postscript)

  • A.A. Freitas & S.H. Lavington. Parallel data mining for very large relational databases. In: H. Liddel et al. (Ed.) LNCS 1067: Proc. Int. Conf. on High-Performance Computing and Networking (HPCN-96, Brussels, Belgium, Apr./96), 158-163. Springer-Verlag, 1996. (postscript)