A Study on the Use of Multiobjective Genetic Algorithms for Classifier Selection in FURIA-based Fuzzy Multiclassifiers
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- Fuzzy rule-based multiclassification systems, bagging, FURIA, genetic selection of individual classifiers, diversity measures, evolutionary multiobjective optimization, NSGA-II
In a preceding contribution, we conducted a study considering a fuzzy multiclassifier system (MCS) design framework based on Fuzzy Unordered Rule Induction Algorithm (FURIA). It served as the fuzzy rule classification learning algorithm to derive the component classifiers considering bagging and feature selection. In this work, we integrate this approach under the overproduce-and-choose strategy. A state-of-the-art evolutionary multiobjective algorithm, namely NSGA-II, is used to provide a component classifier selection and improve FURIA-based fuzzy MCS. We propose five different fitness functions based on three different optimization criteria, accuracy, complexity, and diversity. Twenty UCI high dimensional datasets were considered in order to conduct the experiments. A combination between accuracy and diversity criteria provided very promising results, becoming competitive with classical MCS learning methods.
- © 2017, the Authors. Published by Atlantis Press.
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Cite this article
TY - JOUR AU - Krzysztof TrawiÅ„ski AU - Oscar Cordón AU - Arnaud Quirin PY - 2012 DA - 2012/04/01 TI - A Study on the Use of Multiobjective Genetic Algorithms for Classifier Selection in FURIA-based Fuzzy Multiclassifiers JO - International Journal of Computational Intelligence Systems SP - 231 EP - 253 VL - 5 IS - 2 SN - 1875-6883 UR - https://doi.org/10.1080/18756891.2012.685272 DO - https://doi.org/10.1080/18756891.2012.685272 ID - TrawiÅ„ski2012 ER -