Wrapper-filter feature selection algorithm using a memetic framework
This correspondence presents a novel hybrid wrapper and filter feature selection algorithm for a classification problem using a memetic framework. It incorporates a filter ranking method in the traditional genetic algorithm to improve classification performance and accelerate the search in identifyi...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/148177 |
| _version_ | 1811682354670862336 |
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| author | Zhu, Zexuan Ong, Yew-Soon Dash, Manoranjan |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Zhu, Zexuan Ong, Yew-Soon Dash, Manoranjan |
| author_sort | Zhu, Zexuan |
| collection | NTU |
| description | This correspondence presents a novel hybrid wrapper and filter feature selection algorithm for a classification problem using a memetic framework. It incorporates a filter ranking method in the traditional genetic algorithm to improve classification performance and accelerate the search in identifying the core feature subsets. Particularly, the method adds or deletes a feature from a candidate feature subset based on the univariate feature ranking information. This empirical study on commonly used data sets from the University of California, Irvine repository and microarray data sets shows that the proposed method outperforms existing methods in terms of classification accuracy, number of selected features, and computational efficiency. Furthermore, we investigate several major issues of memetic algorithm (MA) to identify a good balance between local search and genetic search so as to maximize search quality and efficiency in the hybrid filter and wrapper MA. |
| first_indexed | 2024-10-01T03:55:31Z |
| format | Journal Article |
| id | ntu-10356/148177 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:55:31Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1481772021-04-19T03:03:01Z Wrapper-filter feature selection algorithm using a memetic framework Zhu, Zexuan Ong, Yew-Soon Dash, Manoranjan School of Computer Science and Engineering Engineering::Computer science and engineering Chi-square Feature Selection This correspondence presents a novel hybrid wrapper and filter feature selection algorithm for a classification problem using a memetic framework. It incorporates a filter ranking method in the traditional genetic algorithm to improve classification performance and accelerate the search in identifying the core feature subsets. Particularly, the method adds or deletes a feature from a candidate feature subset based on the univariate feature ranking information. This empirical study on commonly used data sets from the University of California, Irvine repository and microarray data sets shows that the proposed method outperforms existing methods in terms of classification accuracy, number of selected features, and computational efficiency. Furthermore, we investigate several major issues of memetic algorithm (MA) to identify a good balance between local search and genetic search so as to maximize search quality and efficiency in the hybrid filter and wrapper MA. Accepted version 2021-04-19T03:03:01Z 2021-04-19T03:03:01Z 2007 Journal Article Zhu, Z., Ong, Y. & Dash, M. (2007). Wrapper-filter feature selection algorithm using a memetic framework. IEEE Transactions On Systems, Man, and Cybernetics, Part B (Cybernetics), 37(1), 70-76. https://dx.doi.org/10.1109/tsmcb.2006.883267 1083-4419 https://hdl.handle.net/10356/148177 10.1109/tsmcb.2006.883267 17278560 1 37 70 76 en IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) © 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TSMCB.2006.883267. application/pdf |
| spellingShingle | Engineering::Computer science and engineering Chi-square Feature Selection Zhu, Zexuan Ong, Yew-Soon Dash, Manoranjan Wrapper-filter feature selection algorithm using a memetic framework |
| title | Wrapper-filter feature selection algorithm using a memetic framework |
| title_full | Wrapper-filter feature selection algorithm using a memetic framework |
| title_fullStr | Wrapper-filter feature selection algorithm using a memetic framework |
| title_full_unstemmed | Wrapper-filter feature selection algorithm using a memetic framework |
| title_short | Wrapper-filter feature selection algorithm using a memetic framework |
| title_sort | wrapper filter feature selection algorithm using a memetic framework |
| topic | Engineering::Computer science and engineering Chi-square Feature Selection |
| url | https://hdl.handle.net/10356/148177 |
| work_keys_str_mv | AT zhuzexuan wrapperfilterfeatureselectionalgorithmusingamemeticframework AT ongyewsoon wrapperfilterfeatureselectionalgorithmusingamemeticframework AT dashmanoranjan wrapperfilterfeatureselectionalgorithmusingamemeticframework |