Journal publications

Refereed journal papers

  1. Vanessa Antunes, Tiemi Sakata, Katti Faceli, and Marcilio C. P. de Souto. 2019. Hybrid strategy for selecting compact set of clustering partitions. Journal of Applied Soft Computing. SJR Q1 2018 (Software)
  2. Ana C. Lorena, Luís P. F. Garcia, Jens Lehmann, Marcilio C. P. de Souto, and Tin Kam Ho. 2019. How Complex Is Your Classification Problem?: A Survey on Measuring Classification Complexity. ACM Computing Surveys 52, 5, Article 107 (September 2019), 34 pages. SJR Q1 2018 (Computer Science)
  3. Marcílio C. P. de Souto, Pablo A. Jaskowiak, and Ivan Costa. 2015. Impact of missing data imputation methods on gene expression clustering and classification. BMC Bioinformatics, 16(1):64. SJR Q1 2015 (Computer Science Applications)
  4. Ana Lorena, Ivan Costa, Newton Spolaôr, and Marcílio C. P. de Souto. 2012. Analysis of complexity indices for classification problems: Cancer gene expression data. Neurocomputing, 75(1):33–42. SJR Q2 2012 (Artificial Intelligence)
  5. Katti Faceli, Tiemi Sakata, Marcílio C. P. de Souto, and André de Carvalho. 2010. Partitions selection strategy for set of clustering solutions. Neurocomputing, 73(16-18):2809–2819. SJR Q2 2010 (Artificial Intelligence)
  6. Katti Faceli, Marcílio C. P. de Souto, and Andre de Carvalho. 2010. Multi-objective clustering ensemble: A framework for cluster analysis. International Journal of Soft Computing and Bioinformatics, 1:9–17.
  7. Katti Faceli, Marcílio C. P. de Souto, Daniel de Araujo, and André de Carvalho. 2009. Multi-objective clustering ensemble for gene expression data analysis. Neurocomputing, 72(13-15):2763–2774. SJR Q2 2009 (Artificial Intelligence)
  8. Teresa B. Ludermir, Marcílio C. P. de Souto, and Wilson Rosa de Oliveira. 2009. On a hybrid weightless neural system. International Journal of Bio-Inspired Computation, 1(1/2):93–104. SJR Q2 2010 (Computer Science)
  9. Marcílio C. P. de Souto, Ivan Costa, Daniel de Araujo, Teresa B. Ludermir, and A. Schliep. 2008. Clustering cancer gene expression data: a comparative study. BMC Bioinformatics, 9. SJR Q1 2008 (Computer Science Applications)
  10. Katti Faceli, André de Carvalho, and Marcílio C. P. de Souto. 2007. Multi-objective clustering ensemble. International Journal of Hybrid Intelligent Systems, 4(3):145–156.
  11. Marcílio C. P. de Souto, Teresa B. Ludermir, and Wilson Rosa de Oliveira. 2005. Equivalence between RAM-based neural networks and probabilistic automata. IEEE Transactions on Neural Networks and Learning Systems, 16(4):996–999. SJR Q1 2005 (Artificial Intelligence)
  12. Ivan Costa, Francisco de Carvalho, and Marcílio C. P. de Souto. 2004. Comparative analysis of clustering methods for gene expression time series data. Genetics and Molecular Biology, 27(4):623–631. SJR Q4 2004 (Molecular Biology)
  13. Ivan Costa, Francisco de Carvalho, and Marcílio C. P. de Souto. 2002. Comparative study of proximity indices for cluster analysis of gene expression time series. Journal of Intelligent and Fuzzy Systems, 13(2-4):133–142. SJR Q4 2002 (Artificial Intelligence)
  14. Wilson Rosa de Oliveira, Marcílio C. P. de Souto, and Teresa B. Ludermir. 2002. Turing’s analysis of computation and artificial neural networks. Journal of Intelligent and Fuzzy Systems, 13(2-4):85–98. SJR Q4 2002 (Artificial Intelligence)
  15. Yamazaki, Teresa B. Ludermir, and Marcílio C. P. de Souto. 2001. Classification of vintages of wine by an artificial nose using time delay neural networks. Electronics Letters, 37(24):1466–1467. SJR Q2 2001 (Electrical and Electronics Engineering)
  16. Marcílio C. P. de Souto, Paulo Adeodato, and Teresa B. Ludermir. 1999. Sequential RAM-based neural networks: Learnability, generalisation, knowledge extraction, and grammatical inference. International Journal of Neural Systems, 9(3):203–210. SJR Q2 1999 (Medicine Miscellaneous)
  17. Teresa B. Ludermir, Antonio Braga, and Marcílio C. P. de Souto. 1999. Weightless neural models: a review of current and past works. Neural Computing Surveys, 2:42–61.