The main advantage of evolutionary multi-objective optimization (EMO) over classical approaches is that a variety of non-dominated solutions with a wide range of objective values can be simultaneously obtained by a single run of an EMO algorithm. In this chapter, we show how this advantage can be utilized in the design of fuzzy ensemble classifiers. First we explain three objectives in multi-objective formulations of fuzzy rule selection. One is accuracy maximization and the others are complexity minimization. Next we demonstrate that a number of non-dominated rule sets (i.e., fuzzy classifiers) are obtained along the accuracy-complexity tradeoff surface from multi-objective fuzzy rule selection problems. Then we examine the effect of combining multiple non-dominated fuzzy classifiers into a single ensemble classifier. Experimental results clearly show that the combination into ensemble classifiers improves the classification ability of individual fuzzy classifiers for some data sets.
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