International conference papers

Refereed international conference papers

  1. Victor H. Barella, Luís P. F. Garcia, Marcilio C. P. de Souto, Ana Carolina Lorena, and André de Carvalho. Data complexity measures for imbalanced classification tasks. In IJCNN, pages 1–8, 2018. (CORE A)
  2. Luís P. F. Garcia, Ana Carolina Lorena, Marcilio C. P. de Souto, and Tin Kam Ho. Classifier recommendation using data complexity measures. In ICPR, pages 874–879. IEEE Computer Society, 2018. (CORE B)
  3. Bruno A. Pimentel, Marcilio C. P. de Souto, and Renata de Souza. Interpreting multivariate membership degrees of fuzzy clustering methods : A strategy. In IJCNN, pages 2800–2804, 2017. (CORE A)
  4. Ana C. Lorena and Marcílio C. P. de Souto. On measuring the complexity of classification problems. In ICONIP, volume 9489 of LNCS, pages 158–167, 2015. (CORE A)
  5. Jane Piantoni, Katti Faceli, Tiemi C. Sakata, Julio C. Pereira, and Marcílio C. P. de Souto. Impact of base partitions on multi-objective and traditional ensemble clustering algorithms. In ICONIP, volume 9489 of LNCS, pages 696–704. Springer, 2015. (CORE A)
  6. Katti Faceli, T. C. Sakata, André de Carvalho, and Marcílio C. P. de Souto. PVis - partitions’ visualizer : Extracting knowledge by visualizing a collection of partitions. In IJCNN, pages 3056–3061, 2014. (CORE A)
  7. Eduardo G. Gusmão and Marcilio C. P. de Souto. Issues on sampling negative examples for predicting prokaryotic promoters. In IJCNN, pages 494–501, 2014. (CORE A)
  8. Marcilio C. P. de Souto, Jose C. M. Oliveira, and Teresa B. Ludermir. A tool to implement probabilistic automata in RAM-based neural networks. In IJCNN, pages 1054–1060, 2011. (CORE A)
  9. Marcílio C. P. de Souto, Ana C. Lorena, Newton Spolaôr, and Ivan G. Costa. Complexity measures of supervised classifications tasks : A case study for cancer gene expression data. In IJCNN, pages 1–7, 2010. (CORE A)
  10. Diogo de Oliveira, Anne Canuto, and Marcílio C. P. de Souto. The diversity/accuracy dilemma : An empirical analysis in the context of heterogeneous ensembles. In IEEE CEC, pages 939–946, 2009. (CORE B)
  11. André C. A. Nascimento, Ricardo Prudêncio, Marcílio C. P. de Souto, and Ivan G. Costa. Mining rules for the automatic selection process of clustering methods applied to cancer gene expression data. In ICANN, volume 5769 of LNCS, pages 20–29. Springer, 2009. (CORE B)
  12. Diogo de Oliveira, Anne Canuto, and Marcílio C. P. de Souto. Use of multi-objective genetic algorithms to investigate the diversity/accuracy dilemma in heterogeneous ensembles. In IJCNN, pages 2339–2346, 2009. (CORE A)
  13. Marcílio C. P. de Souto, Daniel de Araujo, Ivan G. Costa, R. Soares, Teresa B. Ludermir, and A. Schliep. Comparative study on normalization procedures for cluster analysis of gene expression datasets. In IJCNN, pages 2792–2798, 2008. (CORE A)
  14. Marcílio C. P. de Souto, Ricardo Prudêncio, R. Soares, Daniel de Araujo, Ivan Costa, Teresa B. Ludermir, and A. Schliep. Ranking and selecting clustering algorithms using a meta-learning approach. In IJCNN, pages 3729–3735, 2008. (CORE A)
  15. Marcílio C. P. de Souto, R. Soares, A. Santana, and Anne Canuto. Empirical comparison of dynamic classifier selection methods based on diversity and accuracy for building ensembles. In IJCNN, pages 1480–1487, 2008. (CORE A)
  16. Ana Lorena, Ivan Costa, and Marcílio C. P. de Souto. On the complexity of gene expression classification data sets. In HIS, pages 825–830. IEEE Computer Society, 2008. (CORE C)
  17. Karliane Vale, F. Dias, Anne Canuto, and Marcílio C. P. de Souto. A class-based feature selection method for ensemble systems. In HIS, pages 596–601. IEEE Computer Society, 2008. (CORE C)
  18. Diogo de Oliveira, Anne Canuto, and Marcílio C. P. de Souto. Investigating the use of an evolutionary agent-based system for classification tasks. In IJCNN, pages 1266–1271, 2007. (CORE A)
  19. Lucas Oliveira, R. Paradeda, B. Carvalho, Anne Canuto, and Marcílio C. P. de Souto. Particle detection on election microscopy micrographs using multi-classifier systems. In HIS, pages 216–221. IEEE Computer Society, 2007. (CORE C)
  20. Laura Santana, D. de Oliveira, Anne Canuto, and Marcílio C. P. de Souto. A comparative analysis of feature selection methods for ensembles with different combination methods. In IJCNN, pages 643–648, 2007. (CORE A)
  21. Welbson Costa, M. de Assis, and M. C. P. de Souto. Extracting symbolic rules from clustering of gene expression data. In HIS, pages 12–15. IEEE Computer Society, 2006. (CORE C)
  22. Thiago Dutra, Anne Canuto, and Marcílio C. P. de Souto. Using weighted combination-based methods in ensembles with different levels of diversity. In ICONIP, volume 4232 of LNCS, pages 708–717. Springer, 2006. (CORE A)
  23. Marcílio C. P. de Souto, V. Bittencourt, and J. Costa. An empirical analysis of under-sampling techniques to balance a protein structural class dataset. In ICONIP, volume 4234 of LNCS, pages 21–29. Springer, 2006. (CORE A)
  24. Marcílio C. P. de Souto, Daniel de Araujo, and B. da Silva. Cluster ensemble for gene expression microarray data : Accuracy and diversity. In IJCNN, pages 2174–2180, 2006. (CORE A)
  25. Kelly da Silva, M. Monteiro, and Marcílio C. P. de Souto. In silico prediction of promoter sequences of bacillus species. In IJCNN, pages 2319–2324, 2006. (CORE A)
  26. Katti Faceli, André de Carvalho, and Marcílio C. P. de Souto. Multi-objective clustering ensemble. In HIS, page 51. IEEE Computer Society, 2006. (CORE C)
  27. R. Soares, A. Santana, Anne Canuto, and Marcílio C. P. de Souto. Using accuracy and diversity to select classifiers to build ensembles. In IJCNN, pages 1310–1316, 2006. (CORE A)
  28. G. Girelli, S. Azevedo, Marcílio C. P. de Souto, and A. Canuto. Classification of multiple cancer types by individual machine learning techniques and ensemble approaches using gene expression data. In ICONIP, pages 259–263, 2005. (CORE A)
  29. Teresa B. Ludermir, C. Lopes, A. Ludermir, and Marcílio C. P. de Souto. Neural network use for the identification of factors related to common mental disorders. In ICANN, volume 3696 of LNCS, pages 653–658. Springer, 2005. (CORE B)
  30. M. B. Almeida, Marcílio C. P. de Souto, and T. B. Ludermir. Comparative study of connectionist techniques for implementing the pattern recognition system of an artificial nose. In IJCNN, pages 653–656, 2004. (CORE A)
  31. L. Garcia and Marcílio C. P. de Souto. Global optimisation methods for choosing the connectivity pattern of N-tuple classifiers. In IJCNN, pages 2263–2266, 2004. (CORE A)
  32. A. Yamazaki, Marcílio C. P. de Souto, and T. B. Ludermir. Optimization of neural network weights and architectures for odor recognition using simulated annealing. In IJCNN, pages 547–552, 2002. (CORE A)
  33. W. R. de Oliveira, M. C. P. de Souto, and T. B. Ludermir. Agent-environment approach to the simulation of Turing machines by neural networks. In IJCNN, pages 612–620, 2001. (CORE A)
  34. Marcílio C. P. de Souto, W. R. de Oliveira, , and T. B. Ludermir. Computational complexity and pyramidal architectures. In ICONIP, pages 450–458, 2001. (CORE A)
  35. Marcílio C. P. de Souto, Teresa B. Ludermir, and Marcília A. Campos. Encoding of probabilistic automata into RAM-based neural networks. In IJCNN, pages 439–444, 2000. (CORE A)