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http://dx.doi.org/10.3745/JIPS.04.0166

Ensemble of Classifiers Constructed on Class-Oriented Attribute Reduction  

Li, Min (School of Information Engineering, Nanchang Institute of Technology)
Deng, Shaobo (School of Information Engineering, Nanchang Institute of Technology)
Wang, Lei (School of Information Engineering, Nanchang Institute of Technology)
Publication Information
Journal of Information Processing Systems / v.16, no.2, 2020 , pp. 360-376 More about this Journal
Abstract
Many heuristic attribute reduction algorithms have been proposed to find a single reduct that functions as the entire set of original attributes without loss of classification capability; however, the proposed reducts are not always perfect for these multiclass datasets. In this study, based on a probabilistic rough set model, we propose the class-oriented 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 of ensemble constructed on a group of classifiers based on class-oriented reducts with a customized weighted majority voting strategy. We evaluated the performance of our proposed algorithm based on five real multiclass datasets. Experimental results confirm the superiority of the proposed method in terms of four general evaluation metrics.
Keywords
Class-Oriented Attribute Reduction; Ensemble learning; Multiclass Datasets; Probabilistic Rough Sets;
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