Ensemble of Classifiers Constructed on Class-Oriented Attribute Reduction
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Many heuristic attribute reduction algorithms have been proposed to find a single reduct that functions as theentire set of original attributes without loss of classification capability; however, the proposed reducts are notalways perfect for these multiclass datasets. In this study, based on a probabilistic rough set model, we proposethe classoriented attribute reduction (COAR) algorithm, which separately finds a reduct for each target class. Thus, there is a strong dependence between a reduct and its target class. Consequently, we propose a type ofensemble constructed on a group of classifiers based on classoriented reducts with a customized weightedmajority voting strategy. We evaluated the performance of our proposed algorithm based on five real multiclassdatasets. Experimental results confirm the superiority of the proposed method in terms of four generalevaluation metrics.
Abstract
Many heuristic attribute reduction algorithms have been proposed to find a single reduct that functions as theentire set of original attributes without loss of classification capability; however, the proposed reducts are notalways perfect for these multiclass datasets. In this study, based on a probabilistic rough set model, we proposethe classoriented attribute reduction (COAR) algorithm, which separately finds a reduct for each target class. Thus, there is a strong dependence between a reduct and its target class. Consequently, we propose a type ofensemble constructed on a group of classifiers based on classoriented reducts with a customized weightedmajority voting strategy. We evaluated the performance of our proposed algorithm based on five real multiclassdatasets. Experimental results confirm the superiority of the proposed method in terms of four generalevaluation metrics.
참고문헌 (24)
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