Abstract
We have already proposed multiobjective genetic fuzzy rule selection with a multi-classifier coding scheme for the design of ensemble classifiers. An entropy-based diversity measure was used as an objective to be maximized for increasing the diversity of base classifiers in an ensemble. In this paper, we examine the use of other diversity measures in the design of ensemble classifiers. Experimental results show that the choice of a diversity measure has a large effect on the performance of designed ensemble classifiers.
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Nojima, Y., Ishibuchi, H. (2008). Effects of Diversity Measures on the Design of Ensemble Classifiers by Multiobjective Genetic Fuzzy Rule Selection with a Multi-classifier Coding Scheme. In: Corchado, E., Abraham, A., Pedrycz, W. (eds) Hybrid Artificial Intelligence Systems. HAIS 2008. Lecture Notes in Computer Science(), vol 5271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87656-4_93
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DOI: https://doi.org/10.1007/978-3-540-87656-4_93
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