Effective Evolutionary Multilabel Feature Selection under a Budget Constraint

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 improvin...

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Main Authors: Jaesung Lee, Wangduk Seo, Dae-Won Kim
Format: Article
Language:English
Published: Wiley 2018-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2018/3241489
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author Jaesung Lee
Wangduk Seo
Dae-Won Kim
author_facet Jaesung Lee
Wangduk Seo
Dae-Won Kim
author_sort Jaesung Lee
collection DOAJ
description 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.
format Article
id doaj-art-b77b0bd5a1fd4a3dbc07e080faf508d1
institution OA Journals
issn 1076-2787
1099-0526
language English
publishDate 2018-01-01
publisher Wiley
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series Complexity
spelling doaj-art-b77b0bd5a1fd4a3dbc07e080faf508d12025-08-20T02:19:18ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/32414893241489Effective Evolutionary Multilabel Feature Selection under a Budget ConstraintJaesung Lee0Wangduk Seo1Dae-Won Kim2School of Computer Science and Engineering, Chung-Ang University, 221 Heukseok-dong, Dongjak-gu, Seoul 06974, Republic of KoreaSchool of Computer Science and Engineering, Chung-Ang University, 221 Heukseok-dong, Dongjak-gu, Seoul 06974, Republic of KoreaSchool of Computer Science and Engineering, Chung-Ang University, 221 Heukseok-dong, Dongjak-gu, Seoul 06974, Republic of KoreaMultilabel 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.http://dx.doi.org/10.1155/2018/3241489
spellingShingle Jaesung Lee
Wangduk Seo
Dae-Won Kim
Effective Evolutionary Multilabel Feature Selection under a Budget Constraint
Complexity
title Effective Evolutionary Multilabel Feature Selection under a Budget Constraint
title_full Effective Evolutionary Multilabel Feature Selection under a Budget Constraint
title_fullStr Effective Evolutionary Multilabel Feature Selection under a Budget Constraint
title_full_unstemmed Effective Evolutionary Multilabel Feature Selection under a Budget Constraint
title_short Effective Evolutionary Multilabel Feature Selection under a Budget Constraint
title_sort effective evolutionary multilabel feature selection under a budget constraint
url http://dx.doi.org/10.1155/2018/3241489
work_keys_str_mv AT jaesunglee effectiveevolutionarymultilabelfeatureselectionunderabudgetconstraint
AT wangdukseo effectiveevolutionarymultilabelfeatureselectionunderabudgetconstraint
AT daewonkim effectiveevolutionarymultilabelfeatureselectionunderabudgetconstraint