Abstract
Attribute reduction defined by decision error rate in fuzzy rough set model can improve the classification performance of fuzzy rough classifier by deleting redundant attributes, but such reduction does not consider the variations of classification results. To fill this gap, a multi-criterion attribute reduction method is proposed based on four kinds of fuzzy rough set models. This method takes into account both decision error rate and decision consistency. On the basis of the new attribute reduction algorithm, a heuristic algorithm is designed to derive the reduction algorithm. The goal of the algorithm is to obtain smaller error rate and higher consistency at the same time. The experimental results on 4 UCI datasets show that the reducts based on multi-criteria can not only effectively improve the classification accuracies, but also achieve high decision consistencies.
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