Ensemble of Classifiers Constructed on Class-Oriented Attribute Reduction


Min Li, Shaobo Deng, Lei Wang, Journal of Information Processing Systems Vol. 16, No. 2, pp. 360-376, Apr. 2020  

https://doi.org/10.3745/JIPS.04.0166
Keywords: Class-Oriented Attribute Reduction, Ensemble learning, Multiclass Datasets, Probabilistic Rough Sets
Fulltext:

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."


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Cite this article
[APA Style]
Li, M., Deng, S., & Wang, L. (2020). Ensemble of Classifiers Constructed on Class-Oriented Attribute Reduction. Journal of Information Processing Systems, 16(2), 360-376. DOI: 10.3745/JIPS.04.0166.

[IEEE Style]
M. Li, S. Deng, L. Wang, "Ensemble of Classifiers Constructed on Class-Oriented Attribute Reduction," Journal of Information Processing Systems, vol. 16, no. 2, pp. 360-376, 2020. DOI: 10.3745/JIPS.04.0166.

[ACM Style]
Min Li, Shaobo Deng, and Lei Wang. 2020. Ensemble of Classifiers Constructed on Class-Oriented Attribute Reduction. Journal of Information Processing Systems, 16, 2, (2020), 360-376. DOI: 10.3745/JIPS.04.0166.