Feature selection optimization using hybrid relief-f with self-adaptive differential evolution
In various classification areas, the curse of dimensionality becomes a major challenge among the researchers. Thus, feature selection plays an important role in overcoming dimensionality problem. Relief-f is one of the filter methods to rank the most significant features based on their relevance. Al...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Intelligent Networks and Systems Society
2017
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| Online Access: | http://psasir.upm.edu.my/id/eprint/64660/ http://psasir.upm.edu.my/id/eprint/64660/1/Feature%20selection%20optimization%20using%20hybrid%20relief-f%20with%20self-adaptive%20differential%20evolution.pdf |
| _version_ | 1848855061684289536 |
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| author | Zainudin, Muhammad Noorazlan Shah Sulaiman, Md. Nasir Mustapha, Norwati Perumal, Thinagaran Ahmad Nazri, Azree Shahrel Mohamed, Raihani Abd Manaf, Syaifulnizam |
| author_facet | Zainudin, Muhammad Noorazlan Shah Sulaiman, Md. Nasir Mustapha, Norwati Perumal, Thinagaran Ahmad Nazri, Azree Shahrel Mohamed, Raihani Abd Manaf, Syaifulnizam |
| author_sort | Zainudin, Muhammad Noorazlan Shah |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | In various classification areas, the curse of dimensionality becomes a major challenge among the researchers. Thus, feature selection plays an important role in overcoming dimensionality problem. Relief-f is one of the filter methods to rank the most significant features based on their relevance. Although relief-f proved to be a powerful technique in filter strategy, but this method only rank the features based on their significant level. Hence, feature selection is embedded to select the most meaningful features based on their rank. Differential evolution (DE) is one of the evolutionary algorithms that are widely used in various classification domains. Simple and powerful in implementation, we combined relief-f with DE in our proposed feature selection method to solving the optimization problem. In this work, population size and generation size were adaptively determined from the number of features from relief-f. The performance of proposed method is compared with several feature selection techniques in order to prove their superiority using ten datasets obtained from UCI machine learning repository. |
| first_indexed | 2025-11-15T11:19:47Z |
| format | Article |
| id | upm-64660 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:19:47Z |
| publishDate | 2017 |
| publisher | Intelligent Networks and Systems Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-646602018-08-13T03:45:45Z http://psasir.upm.edu.my/id/eprint/64660/ Feature selection optimization using hybrid relief-f with self-adaptive differential evolution Zainudin, Muhammad Noorazlan Shah Sulaiman, Md. Nasir Mustapha, Norwati Perumal, Thinagaran Ahmad Nazri, Azree Shahrel Mohamed, Raihani Abd Manaf, Syaifulnizam In various classification areas, the curse of dimensionality becomes a major challenge among the researchers. Thus, feature selection plays an important role in overcoming dimensionality problem. Relief-f is one of the filter methods to rank the most significant features based on their relevance. Although relief-f proved to be a powerful technique in filter strategy, but this method only rank the features based on their significant level. Hence, feature selection is embedded to select the most meaningful features based on their rank. Differential evolution (DE) is one of the evolutionary algorithms that are widely used in various classification domains. Simple and powerful in implementation, we combined relief-f with DE in our proposed feature selection method to solving the optimization problem. In this work, population size and generation size were adaptively determined from the number of features from relief-f. The performance of proposed method is compared with several feature selection techniques in order to prove their superiority using ten datasets obtained from UCI machine learning repository. Intelligent Networks and Systems Society 2017 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/64660/1/Feature%20selection%20optimization%20using%20hybrid%20relief-f%20with%20self-adaptive%20differential%20evolution.pdf Zainudin, Muhammad Noorazlan Shah and Sulaiman, Md. Nasir and Mustapha, Norwati and Perumal, Thinagaran and Ahmad Nazri, Azree Shahrel and Mohamed, Raihani and Abd Manaf, Syaifulnizam (2017) Feature selection optimization using hybrid relief-f with self-adaptive differential evolution. International Journal of Intelligent Engineering and Systems, 10 (2). pp. 21-29. ISSN 2185-3118 http://www.inass.org/abstract2017/ijies2017043003.html 10.22266/ijies2017.0430.03 |
| spellingShingle | Zainudin, Muhammad Noorazlan Shah Sulaiman, Md. Nasir Mustapha, Norwati Perumal, Thinagaran Ahmad Nazri, Azree Shahrel Mohamed, Raihani Abd Manaf, Syaifulnizam Feature selection optimization using hybrid relief-f with self-adaptive differential evolution |
| title | Feature selection optimization using hybrid relief-f with self-adaptive differential evolution |
| title_full | Feature selection optimization using hybrid relief-f with self-adaptive differential evolution |
| title_fullStr | Feature selection optimization using hybrid relief-f with self-adaptive differential evolution |
| title_full_unstemmed | Feature selection optimization using hybrid relief-f with self-adaptive differential evolution |
| title_short | Feature selection optimization using hybrid relief-f with self-adaptive differential evolution |
| title_sort | feature selection optimization using hybrid relief-f with self-adaptive differential evolution |
| url | http://psasir.upm.edu.my/id/eprint/64660/ http://psasir.upm.edu.my/id/eprint/64660/ http://psasir.upm.edu.my/id/eprint/64660/ http://psasir.upm.edu.my/id/eprint/64660/1/Feature%20selection%20optimization%20using%20hybrid%20relief-f%20with%20self-adaptive%20differential%20evolution.pdf |