Feature selection refers to finding a compact feature subset in which redundant and irrelevant information is reduced. It is generally aimed at simplifying the learning model and improving the learning performance simultaneously. To further enhance the traditional feature selection, ensemble feature selection has been developed vigorously in recent years. In this study, a novel ensemble feature selection algorithm that fuses multiple scores induced by multiple neighbourhood rough set–based feature evaluation criteria is presented. In detail, three different neighbourhood rough set–based feature evaluation criteria are applied, including neighbourhood approximation quality, neighbourhood recognition rate, and neighbourhood discrimination index. These representative measures serve as ensemble bases to induce multiple feature scores. Then, an aggregation method is proposed to fuse these scores for quantifying the feature importance quality. Finally, a searching algorithm based on this neighbourhood score by multicriterion fusion is devised. Experimental results on 16 UCI data sets prove that the proposed method outperforms other feature selection algorithms in classification task.

Zhen Ma

Journal of Applied Mathematics

JOURNAL OF APPLIED MATHEMATICS 是一本专业期刊，致力于在应用、计算和工业数学的所有领域发表原创研究论文和评论文章。

The Journal of Applied Mathematics is a professional journal dedicated to publishing original research papers and review articles in all areas of applied, computational, and industrial mathematics.

《Journal of Applied Mathematics》是一本专业期刊，专注于应用、计算和工业数学领域，发表原创研究论文和评论文章。

Ensemble Feature Selection Using Neighbourhood Rough Set–Based Multicriterion Fusion

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Journal Info

Category	Quartile
MATHEMATICS, INTERDISCIPLINARY APPLICATIONS	3