Effective Evolutionary Multilabel Feature Selection under a Budget Constraint. (7th March 2018)

• Record Type: Journal Article
• Authors: Lee, Jaesung; Seo, Wangduk; Kim, Dae-Won
• Other Names: Wong Kevin Academic Editor.
• Is Part Of: Complexity. Volume 2018(2018)
• Publication Date: 2018-03-07
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2018/3241489
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : Multilabel feature selection involves the selection of relevant features from multilabeled datasets, resulting in improved multilabel learning accuracy. Evolutionary search-based multilabel feature selection methods have proved useful for identifying a compact feature subset by successfully improving the accuracy of multilabel classification. However, conventional methods frequently violate budget constraints or result in inefficient searches due to ineffective exploration of important features. In this paper, we present an effective evolutionary search-based feature selection method for multilabel classification with a budget constraint. The proposed method employs a novel exploration operation to enhance the search capabilities of a traditional genetic search, resulting in improved multilabel classification. Empirical studies using 20 real-world datasets demonstrate that the proposed method outperforms conventional multilabel feature selection methods.