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Volume 2018 (2018), Article ID 3241489, 14 pages
Research Article

Effective Evolutionary Multilabel Feature Selection under a Budget Constraint

School of Computer Science and Engineering, Chung-Ang University, 221 Heukseok-dong, Dongjak-gu, Seoul 06974, Republic of Korea

Correspondence should be addressed to Dae-Won Kim;

Received 29 September 2017; Accepted 21 January 2018; Published 7 March 2018

Academic Editor: Kevin Wong

Copyright © 2018 Jaesung Lee et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.