Ensemble of feature selection methods: A hesitant fuzzy sets approach

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• 作者：Mohammad Kazem EbrahimpourAuthor Vitae ; Mahdi Eftekhari ; ^{m.eftekhari@uk.ac.ir}Author Vitae
• 关键词：Hesitant fuzzy sets ; Feature selection ; High dimensional datasets ; Big data ; Imbalanced datasets ; Microarray datasets
• 刊名：Applied Soft Computing
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：50
• 期：Complete
• 页码：300-312
• 全文大小：3366 K
• 卷排序：50

文摘

In this paper, hesitant fuzzy sets are utilized for representing ensemble of ranking algorithms (as a relevancy measure) and ensemble of similarity measures (as a redundancy merit) for feature subset selection. In this paper, the well-known CFS merit has been fuzzified with ensemble of feature ranking algorithms and similarity measures. The proposed MRMR-HFS is recmmended when one deals with high dimensional datasets which suffer from small sample sizes. Moreover, it can be used when speed of feature selection process is matter. The proposed MRMR-HFS method can be used when the search space is extremely large and cannot be searched by meta-heuristic algorithms. Several experimental results as well as non-parametric statistical tests confirm the performance of our MRMR-HFS method in the field of feature selection.