Improving the Performance of Fuzzy Rule Based Classification Systems for Highly Imbalanced Data-Sets Using an Evolutionary Adaptive Inference System - researchr publication related

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Alberto Fernández, María José del Jesús, Francisco Herrera. Improving the Performance of Fuzzy Rule Based Classification Systems for Highly Imbalanced Data-Sets Using an Evolutionary Adaptive Inference System. In Joan Cabestany, Francisco Sandoval, Alberto Prieto, Juan M. Corchado, editors, Bio-Inspired Systems: Computational and Ambient Intelligence, 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Salamanca, Spain, June 10-12, 2009. Proceedings, Part I. Volume 5517 of Lecture Notes in Computer Science, pages 294-301, Springer, 2009. [doi]

The following publications are possibly variants of this publication:

On the influence of an adaptive inference system in fuzzy rule based classification systems for imbalanced data-setsAlberto FernÃ¡ndez, MarÃa JosÃ© del JesÃºs, Francisco Herrera. eswa, 36(6):9805-9812, 2009. [doi]

A Genetic Algorithm for Feature Selection and Granularity Learning in Fuzzy Rule-Based Classification Systems for Highly Imbalanced Data-SetsPedro Villar, Alberto Fernández, Francisco Herrera. ipmu 2010: 741-750 [doi]

On the 2-tuples based genetic tuning performance for fuzzy rule based classification systems in imbalanced data-setsAlberto Fernández, María José del Jesús, Francisco Herrera. isci, 180(8):1268-1291, 2010. [doi]

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