Parallel Selector for Feature Reduction

Zhenyu Yin, Yan Fan, Pingxin Wang and Jianjun Chen ()
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Zhenyu Yin: School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China
Yan Fan: School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China
Pingxin Wang: School of Science, Jiangsu University of Science and Technology, Zhenjiang 212100, China
Jianjun Chen: School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China

Mathematics, 2023, vol. 11, issue 9, 1-33

Abstract: In the field of rough set, feature reduction is a hot topic. Up to now, to better guide the explorations of this topic, various devices regarding feature reduction have been developed. Nevertheless, some challenges regarding these devices should not be ignored: (1) the viewpoint provided by a fixed measure is underabundant; (2) the final reduct based on single constraint is sometimes powerless to data perturbation; (3) the efficiency in deriving the final reduct is inferior. In this study, to improve the effectiveness and efficiency of feature reduction algorithms, a novel framework named parallel selector for feature reduction is reported. Firstly, the granularity of raw features is quantitatively characterized. Secondly, based on these granularity values, the raw features are sorted. Thirdly, the reordered features are evaluated again. Finally, following these two evaluations, the reordered features are divided into groups, and the features satisfying given constraints are parallel selected. Our framework can not only guide a relatively stable feature sequencing if data perturbation occurs but can also reduce time consumption for feature reduction. The experimental results over 25 UCI data sets with four different ratios of noisy labels demonstrated the superiority of our framework through a comparison with eight state-of-the-art algorithms.

Keywords: rough set; feature reduction; feature evaluation; data perturbation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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