Leandro L. Minku's Refereed Journal Papers

Copyright: The copyright of the papers below is owned by the respective publishers.  Personal use of the electronic versions here provided is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the publishers.
  1. WANG, S.; MINKU, L.; YAO, X. . "A Systematic Study of Online Class Imbalance Learning with Concept Drift", (currently under review; access to article at arXiv).

  2. KRAWCZYK, B.; MINKU, L.L.; GAMA, J.; STEFANOWSKI, J.; WOZNIAK, M. . "Ensemble Learning for Data Stream Analysis: a survey", Information Fusion, v. 37, p. 132-156, January 2017. Paper also available here
  3. MINKU, L.L.; YAO, X. . "Which Models of the Past Are Relevant to the Present? A software effort estimation approach to exploiting useful past models", Automated Software Engineering Journal, December 2016 (accepted), doi: 10.1007/s10515-016-0209-7. Paper also available here.
  4. CONSOLI, P.A.; MEI, Y.; MINKU, L.L.; YAO, X. . "Dynamic Selection of Evolutionary Operators Based on Online Learning and Fitness Landscape Analysis" Soft Computing, v. 20, n. 10, p. 3889-3914, October 2016, doi: 10.1007/s00500-016-2126-x. Paper also available here.
  5. MINKU, L.L.; MENDES, E.; TURHAN, B. . "Data Mining for Software Engineering and Humans in the Loop", Progress in Artificial Intelligence (PRAI), v. 5, n. 4, p. 307-314, April 2016, doi: 10.1007/s13748-016-0092-2. Paper available as open source here.
  6. SUN, Y.; TANG, K.; MINKU, L.L.; WANG, S.; YAO, X. . "Online Ensemble Learning of Data Streams with Gradually Evolved Classes", IEEE Transactions on Knowledge and Data Engineering, v. 28, n. 6, p. 1532-1545, February 2016, doi: 10.1109/TKDE.2016.2526675. Paper also available here.
  7. SHEN, X.-N., MINKU, L.L., BAHSOON, R. and YAO, X. "Dynamic Software Project Scheduling through a Proactive-rescheduling Method", IEEE Transactions on Software Engineering, v.42, n. 7, p. 658-686, December 2015, doi: 10.1109/TSE.2015.2512266. Paper also available here.(Among 5 most downloaded IEEE TSE papers in July--September 2016)
  8. AZZEH, M.; NASSIF, A.B.; MINKU, L.L. . "An Empirical Evaluation of Ensemble Adjustment Methods for Analogy-Based Effort Estimation", Journal of Systems and Software, Elsevier, v. 103, p. 36-52, May 2015, doi:10.1016/j.jss.2015.01.028.
  9. WANG, S.; MINKU, L. L.; YAO, X. . "Resampling-Based Ensemble Methods for Online Class Imbalance Learning", IEEE Transactions on Knowledge and Data Engineering, IEEE, v. 27, n. 5, p. 1356-1368, May 2015, doi: 10.1109/TKDE.2014.2345380. Paper available via IEEE open access here.

  10. MINKU, L. L.; SUDHOLT, D.; YAO, X. . "Improved Evolutionary Algorithm Design for the Project Scheduling Problem Based on Runtime Analysis", IEEE Transactions on Software Engineering, IEEE, v. 40, n. 1, p. 83-102, January 2014, doi: 10.1109/TSE.2013.52 (2nd most popular TSE article in March 2014 in terms of number of downloads among all TSE papers published online; remained among the 15 most popular until the most recent statistics checked in August 2014). Paper available via IEEE open access here.

  11. WANG, S.; MINKU, L. L.; YAO, X. . "Online Class Imbalance Learning and Its Applications in Fault Detection", International Journal of Computational Intelligence and Applications, Imperial College Press, v. 12, n. 4, article no. 1340001, 19p., December 2013, doi: 10.1142/S1469026813400014. Paper also available here.

  12. MINKU, L. L.; YAO, X. . "Software Effort Estimation as a Multi-objective Learning Problem", ACM Transactions on Software Engineering and Methodology, ACM, v. 22, n. 4, article no. 35, 32p., October 2013, doi: 10.1145/2522920.2522928 (9th most popular TOSEM article in March 2014 in terms of number of downloads among all TSE papers published online). Paper available here (ACM Author-Izer). Link to preprocessed PROMISE data sets used in the study here.

  13. LI, Y.; HU, C.; MINKU, L. L.; ZUO, H. . "Learning Aesthetic Judgements in Evolutionary Art Systems.", Genetic Programming and Evolvable Machines (GENP), Special Issue on Evolutionary Computation in Art, Sound and Music, Springer, v. 14, n. 3, p. 315-337, September 2013, DOI: 10.1007/s10710-013-9188-7.

  14. MINKU, L. L.; YAO, X. . "Ensembles and Locality: Insight on Improving Software Effort Estimation.", Information and Software Technology, Special Issue on Best Papers from PROMISE 2011, Elsevier, v. 55, n. 8, p. 1512-1528, August 2013, doi: 10.1016/j.infsof.2012.09.012. Paper also available here. Link to preprocessed PROMISE data sets used in the study here.

  15. ZLIOBAITE, I.; BIFET, A.; GABER; M.; GABRYS, B.; GAMA, J.; MINKU, L.; MUSIAL, K. . "Next Challenges for Adaptive Learning Systems.", ACM SIGKDD Explorations Newsletter, ACM, v. 14, n. 1, p. 48-55., June 2012, doi: 10.1145/2408736.2408746. Paper available here (ACM Author-Izer).

  16. MINKU, L. L.; YAO, X. . "DDD: A New Ensemble Approach For Dealing With Concept Drift.", IEEE Transactions on Knowledge and Data Engineering, IEEE, v. 24, n. 4, p. 619-633, April  2012, doi: 10.1109/TKDE.2011.58. Paper also available here. DDD's prequential accuracy and standard deviation result files here. (>100 citations according to Google Scholar)

  17. ZANCHETTIN, C.; MINKU, L. L.; LUDERMIR, T. B. . "Design of Experiments in Neuro-Fuzzy Systems",  International Journal of Computational Intelligence and Applications (IJCIA), Imperial College Press, v. 9, n. 2, p. 137-152, June 2010, doi: 10.1142/S1469026810002823. Paper also available here.

  18. MINKU, L. L.; WHITE, A. P.; YAO, X. . "The Impact of Diversity on On-line Ensemble Learning in the Presence of Concept Drift.", IEEE Transactions on Knowledge and Data Engineering, IEEE, v. 22, n. 5, p. 730-742, May  2010, doi: 10.1109/TKDE.2009.156 (>150 citations according to Google Scholar). Paper also available here. Link to data sets here.

  19. TANG, K.; LIN, M.; MINKU, L.; YAO, X. . "Selective Negative Correlation Learning Approach to Incremental Learning.", Neurocomputing, v. 72, n.13-15, p. 2796-2805, Elsevier, August 2009, doi: 10.1016/j.neucom.2008.09.022.

  20. MINKU, L.; INOUE, H.; YAO, X. . "Negative Correlation in Incremental Learning", Natural Computing Journal - Special Issue on Nature-inspired Learning and Adaptive Systems, v. 8, n. 2, p. 289-320, Springer, June 2009, doi: 10.1007/s11047-007-9063-7. Paper also available here.

  21. MINKU, L.; LUDERMIR, T. B. . "Clustering and Co-evolution to Construct Neural Network Ensembles: an experimental study", Neural Networks, v. 21, n. 9, p. 1363-1379, Elsevier, November 2008, doi:10.1016/j.neunet.2008.02.001. Paper also available here

  22. MINKU, L.; POZO, A. T. R.; VERGILIO, S. R. . "Chameleon: uma ferramenta de programacao genetica orientada a gramaticas". Revista Eletronica de Iniciacao Cientifica, v. 3, n. 2, 15p., 2003, ISSN 1519-8219. (in Portuguese)