Leandro L. Minku

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Publications

Copyright ©: The copyright of the papers below is owned by the respecteive 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.

Refereed Journal Papers

  1. MINKU, L. L.; YAO, X. . "Software Effort Estimation as a Multi-objective Learning Problem", ACM Transactions on Software Engineering and Methodology, ACM, 32p., 2012. Final author's version available here. (in press)

  2. 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, 2013, DOI: 10.1007/s10710-013-9188-7.

  3. 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, 2013, doi: 10.1016/j.infsof.2012.09.012. Paper also available here.

  4. ZLIOBAITE, I.; BIFET, A.; GABER; M.; GABRYS, B.; GAMA, J.; MINKU, L.; MUSIAL, K. . "Next Challenges for Adaptive Learning Systems.", SIGKDD Explorations, ACM, v. 14, n. 1, p. 48-55., 2012, ISSN 1931-0145. Paper available here.

  5. 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, 2012, doi: 10.1109/TKDE.2011.58. Paper also available here. DDD's prequential accuracy and standard deviation result files here.

  6. 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, 2010, doi: 10.1142/S1469026810002823. Paper also available here.

  7. 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, 2010, doi: 10.1109/TKDE.2009.156. Paper also available here. Link to data sets here.

  8. 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, 2009, doi: 10.1016/j.neucom.2008.09.022.

  9. 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, 2009, doi: 10.1007/s11047-007-9063-7. Paper also available here.

  10. 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, 2008, doi:10.1016/j.neunet.2008.02.001. Paper also available here. 

  11. MINKU, L.; POZO, A. T. R.; VERGILIO, S. R. . “Chameleon: uma ferramenta de programação genética orientada a gramáticas”. Revista Eletrônica de Iniciação Científica, v. 3, n. 2, 15p., 2003, ISSN 1519-8219. (in Portuguese)


Refereed Conference Papers

  1. SONG, L.; MINKU, L. L.; YAO, X.; "The Impact of Parameter Tuning on Software Effort Estimation Using Learning Machines", 9th International Conference on Predictive Models in Software Engineering (PROMISE'13), October 2013 (accepted).

  2. MINKU, L. L.; YAO, X.; "An Analysis of Multi-objective Evolutionary Algorithms for Training Ensemble Models Based on Different Performance Measures in Software Effort Estimation", 9th International Conference on Predictive Models in Software Engineering (PROMISE'13), October 2013 (accepted).

  3. WANG, S.; MINKU, L.; GHEZZI, D.; CALTABIANO, D.; TINO, P.; YAO, X. . “Concept Drift Detection for Online Class Imbalance Learning”, International Joint Conference on Neural Networks (IJCNN) 2013 (accepted). Paper also available here.

  4. WANG, S.; MINKU, L.; YAO, X. . “A Learning Framework for Online Class Imbalance Learning”,  IEEE Symposium on Computational Intelligence and Ensemble Learning (CIEL), at IEEE Symposium Series on Computational Intelligence (SSCI), p. 36-45, Singapore, 2013. Paper also available here.

  5. MINKU, L.; YAO, X. . “Can Cross-company Data Improve Performance in Software Effort Estimation?”, Proceedings of the 8th International Conference on Predictive Models in Software Engineering (PROMISE'2012), p. 69-78, Lund, Sweden, September 2012. Paper also available here.

  6. MINKU, L.; SUDHOLT, D.; YAO, X. . “Evolutionary Algorithms for the Project Scheduling Problem: Runtime Analysis and Improved Design”, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'2012), p. 1221-1228, Philadelphia, July 2012. Paper also available here. 

  7. MINKU, L.; YAO, X. . “Using Unreliable Data for Creating More Reliable Online Learners”, Proceedings of the International Joint Conference on Neural Networks (IJCNN’2012), p. 2492-2499, Brisbane, Australia, June 2012. Paper also available here.

  8. MINKU, L.; YAO, X. . “A Principled Evaluation of Ensembles of Learning Machines for Software Effort Estimation”, Proceedings of the 7th International Conference on Predictive Models in Software Engineering (PROMISE'2011), Banff, Canada, September 2011. Paper also available here. Presentation available here.

  9. MINKU, L.; YAO, X. . “Using Diversity to Handle Concept Drift in On-line Learning”, Proceedings of the International Joint Conference on Neural Networks (IJCNN’2009), p. 2125-2132, Atlanta, June 2009.

  10. MINKU, L.; YAO, X. . “On-line Bagging Negative Correlation Learning”, Proceedings of the International Joint Conference on Neural Networks (IJCNN’2008), Part III, p. 1375-1382, Hong Kong, June 2008. Paper also available here.

  11. MINKU, L.; LUDERMIR, T. B. . “EFuNN Ensembles Construction Using a Clustering Method and a Coevolutionary Multi-Objective Genetic Algorithm”, Proceedings of the 3th International Conference on Neural Information Processing (ICONIP’2006), Part III, Lecture Notes in Computer Science 4234, p. 884-891, Hong Kong, October 2006. Paper also available here. 

  12. MINKU, L.; LUDERMIR, T. B. . “EFuNN Ensembles Construction Using CONE with Multi-objective GA”, Proceedings of the IX Brazilian Neural Networks Symposium (SBRN'2006), p. 48–53, Ribeirão Preto, Brazil, October 2006.  Paper also available here.

  13. MINKU, L.; LUDERMIR, T. B. . “EFuNNs Ensembles Construction Using a Clustering Method and a Coevolutionary Genetic Algorithm”, Proceedings of the 2006 IEEE Congress on Evolutionary Computation (CEC’2006), p. 1399-1406, Vancouver, Canada, July 2006. Paper also available here. 

  14. ZANCHETTIN, C.; MINKU, L.; LUDERMIR, T. B. . “Design of Experiments in Neuro-Fuzzy Systems”, Proceedings of the 5th International Conference on Hybrid Intelligent Systems (HIS'2005), 6p., Rio de Janeiro, Brazil, November 2005. Extended IJCIA version here.

  15. MINKU, L.; LUDERMIR, T. B. . “Estratégia Evolucionária e Algoritmos Genéticos para Otimização Dinâmica de Parâmetros de EFuNNs.”, In:  VII Congresso Brasileiro de Redes Neurais (CBRN’2005), 6p., Natal, Rio Grande do Norte, Brazil, October 2005. (in Portuguese)

  16. MINKU, L.; LUDERMIR, T. B. . “Evolutionary Strategies and Genetic Algorithms for Dynamic Parameter Optimization of Evolving Fuzzy Neural Networks”, Proceedings of the 2005 IEEE Congress on Evolutionary Computation (CEC’2005), v. 3, p. 1951-1958, Edinburgh, Scotland, September 2005. Paper also available here. 

  17. MINKU, L.; LUDERMIR, T. B.; ARAÚJO, A. F. R. . “Computação Evolucionária para otimização dinâmica de parâmetros de EFuNNs”, In: V Encontro Nacional de Inteligência Artificial (ENIA’2005), p. 612-621, São Leopoldo, Rio Grande do Sul, Brazil, July 2005. Paper also available here. (in Portuguese)


Tutorials

  1. MENZIES, T.; KOCAGUNELI, E.; PETERS, F.; TURHAN, B.; MINKU, L.L. . “Data Science for Software Engineering”, In: International Conference on Software Engineering (ICSE'2013), San Francisco, May 2013.


Abstracts

  1. MINKU, L.; VERGILIO, S. R.. “Utilizando Evolução para Estabelecer Estratégias de Teste.”, In: X Evento de Iniciação Científica da UFPR (EVINCI’2002). Anais do X EVINCI, v. 1, p. 5-5, Curitiba, Paraná, Brazil: Editora da UFPR, 2002. (in Portuguese)