A Clustering-Guided Integer Brain Storm Optimizer for Feature Selection in High-Dimensional Data
For high-dimensional data with a large number of redundant features, existing feature selection algorithms still have the problem of “curse of dimensionality.” In view of this, the paper studies a new two-phase evolutionary feature selection algorithm, called clustering-guided integer brain storm op...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2021/8462493 |
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| author | Jia Yun-Tao Zhang Wan-Qiu He Chun-Lin |
| author_facet | Jia Yun-Tao Zhang Wan-Qiu He Chun-Lin |
| author_sort | Jia Yun-Tao |
| collection | DOAJ |
| description | For high-dimensional data with a large number of redundant features, existing feature selection algorithms still have the problem of “curse of dimensionality.” In view of this, the paper studies a new two-phase evolutionary feature selection algorithm, called clustering-guided integer brain storm optimization algorithm (IBSO-C). In the first phase, an importance-guided feature clustering method is proposed to group similar features, so that the search space in the second phase can be reduced obviously. The second phase applies oneself to finding optimal feature subset by using an improved integer brain storm optimization. Moreover, a new encoding strategy and a time-varying integer update method for individuals are proposed to improve the search performance of brain storm optimization in the second phase. Since the number of feature clusters is far smaller than the size of original features, IBSO-C can find an optimal feature subset fast. Compared with several existing algorithms on some real-world datasets, experimental results show that IBSO-C can find feature subset with high classification accuracy at less computation cost. |
| format | Article |
| id | doaj-art-75932c502f944ff28c8583d2bee33500 |
| institution | OA Journals |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-75932c502f944ff28c8583d2bee335002025-08-20T02:07:00ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2021-01-01202110.1155/2021/84624938462493A Clustering-Guided Integer Brain Storm Optimizer for Feature Selection in High-Dimensional DataJia Yun-Tao0Zhang Wan-Qiu1He Chun-Lin2Zhuhai Campus, Beijing Institute of Technology, Zhuhai, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou, ChinaFor high-dimensional data with a large number of redundant features, existing feature selection algorithms still have the problem of “curse of dimensionality.” In view of this, the paper studies a new two-phase evolutionary feature selection algorithm, called clustering-guided integer brain storm optimization algorithm (IBSO-C). In the first phase, an importance-guided feature clustering method is proposed to group similar features, so that the search space in the second phase can be reduced obviously. The second phase applies oneself to finding optimal feature subset by using an improved integer brain storm optimization. Moreover, a new encoding strategy and a time-varying integer update method for individuals are proposed to improve the search performance of brain storm optimization in the second phase. Since the number of feature clusters is far smaller than the size of original features, IBSO-C can find an optimal feature subset fast. Compared with several existing algorithms on some real-world datasets, experimental results show that IBSO-C can find feature subset with high classification accuracy at less computation cost.http://dx.doi.org/10.1155/2021/8462493 |
| spellingShingle | Jia Yun-Tao Zhang Wan-Qiu He Chun-Lin A Clustering-Guided Integer Brain Storm Optimizer for Feature Selection in High-Dimensional Data Discrete Dynamics in Nature and Society |
| title | A Clustering-Guided Integer Brain Storm Optimizer for Feature Selection in High-Dimensional Data |
| title_full | A Clustering-Guided Integer Brain Storm Optimizer for Feature Selection in High-Dimensional Data |
| title_fullStr | A Clustering-Guided Integer Brain Storm Optimizer for Feature Selection in High-Dimensional Data |
| title_full_unstemmed | A Clustering-Guided Integer Brain Storm Optimizer for Feature Selection in High-Dimensional Data |
| title_short | A Clustering-Guided Integer Brain Storm Optimizer for Feature Selection in High-Dimensional Data |
| title_sort | clustering guided integer brain storm optimizer for feature selection in high dimensional data |
| url | http://dx.doi.org/10.1155/2021/8462493 |
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