Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection
Multi-label feature selection is designed to select a subset of features according to their importance to multiple labels. This task can be achieved by ranking the dependencies of features and selecting the features with the highest rankings. In a multi-label feature selection problem, the algorithm...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-11-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/18/11/405 |
| _version_ | 1811300237320388608 |
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| author | Jaesung Lee Dae-Won Kim |
| author_facet | Jaesung Lee Dae-Won Kim |
| author_sort | Jaesung Lee |
| collection | DOAJ |
| description | Multi-label feature selection is designed to select a subset of features according to their importance to multiple labels. This task can be achieved by ranking the dependencies of features and selecting the features with the highest rankings. In a multi-label feature selection problem, the algorithm may be faced with a dataset containing a large number of labels. Because the computational cost of multi-label feature selection increases according to the number of labels, the algorithm may suffer from a degradation in performance when processing very large datasets. In this study, we propose an efficient multi-label feature selection method based on an information-theoretic label selection strategy. By identifying a subset of labels that significantly influence the importance of features, the proposed method efficiently outputs a feature subset. Experimental results demonstrate that the proposed method can identify a feature subset much faster than conventional multi-label feature selection methods for large multi-label datasets. |
| first_indexed | 2024-04-13T06:49:13Z |
| format | Article |
| id | doaj.art-33e4b78db4b54797bd5c394f2d3a632c |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-13T06:49:13Z |
| publishDate | 2016-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-33e4b78db4b54797bd5c394f2d3a632c2022-12-22T02:57:28ZengMDPI AGEntropy1099-43002016-11-01181140510.3390/e18110405e18110405Efficient Multi-Label Feature Selection Using Entropy-Based Label SelectionJaesung Lee0Dae-Won Kim1School of Computer Science and Engineering, Chung-Ang University, 221 Heukseok-Dong, Dongjak-Gu, Seoul 156-756, KoreaSchool of Computer Science and Engineering, Chung-Ang University, 221 Heukseok-Dong, Dongjak-Gu, Seoul 156-756, KoreaMulti-label feature selection is designed to select a subset of features according to their importance to multiple labels. This task can be achieved by ranking the dependencies of features and selecting the features with the highest rankings. In a multi-label feature selection problem, the algorithm may be faced with a dataset containing a large number of labels. Because the computational cost of multi-label feature selection increases according to the number of labels, the algorithm may suffer from a degradation in performance when processing very large datasets. In this study, we propose an efficient multi-label feature selection method based on an information-theoretic label selection strategy. By identifying a subset of labels that significantly influence the importance of features, the proposed method efficiently outputs a feature subset. Experimental results demonstrate that the proposed method can identify a feature subset much faster than conventional multi-label feature selection methods for large multi-label datasets.http://www.mdpi.com/1099-4300/18/11/405multi-label feature selectionlabel selectionmutual informationentropy |
| spellingShingle | Jaesung Lee Dae-Won Kim Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection Entropy multi-label feature selection label selection mutual information entropy |
| title | Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection |
| title_full | Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection |
| title_fullStr | Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection |
| title_full_unstemmed | Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection |
| title_short | Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection |
| title_sort | efficient multi label feature selection using entropy based label selection |
| topic | multi-label feature selection label selection mutual information entropy |
| url | http://www.mdpi.com/1099-4300/18/11/405 |
| work_keys_str_mv | AT jaesunglee efficientmultilabelfeatureselectionusingentropybasedlabelselection AT daewonkim efficientmultilabelfeatureselectionusingentropybasedlabelselection |