A Combination Scheme for Fuzzy Partitions Based on Fuzzy Weighted Majority Voting Rule
Chunsheng Li, Yaonan Wang, Hongliang Dai · 2009
This paper devotes to the combination of fuzzy partitions with the same number of clusters by means of generalizing the weighted majority voting rule to fuzzy weighted majority voting rule. The difficulties of this generalization are to establish the correspondences among the classes and determine the weight coefficients of component fuzzy partitions. We propose a class-matching algorithm based on Hungarian method and generalize pattern recognition rate to fuzzy pattern recognition rate to overcome the difficulties. Employing the proposed class-matching algorithm and the fuzzy weighted majority voting rule, a combining scheme for fuzzy partitions is developed. Experimental results on real datasets show that the proposed ensemble of fuzzy partitions outperforms or is comparable to other two existed ensembles of fuzzy partitions in terms of most evaluation indexes for fuzzy partition.