A Novel Hybrid Algorithm for Feature Selection Based on Whale Optimization Algorithm
Feature selection enhances classification accuracy by removing irrelevant and redundant feature. Feature selection plays an important role in data mining and pattern recognition. In this paper, we propose a hybrid feature subset selection algorithm called the maximum Pearson maximum distance improve...
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| Vydané v: | IEEE access Ročník 7; s. 14908 - 14923 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2169-3536, 2169-3536 |
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| Abstract | Feature selection enhances classification accuracy by removing irrelevant and redundant feature. Feature selection plays an important role in data mining and pattern recognition. In this paper, we propose a hybrid feature subset selection algorithm called the maximum Pearson maximum distance improved whale optimization algorithm (MPMDIWOA). First, based on Pearson's correlation coefficient and correlation distance, a filter algorithm is proposed named maximum Pearson maximum distance (MPMD). Two parameters are proposed in MPMD to adjust the weights of the relevance and redundancy. Second, the modified whale optimization algorithm can act as a wrapper algorithm. After introducing the maximum value without change (MVWC) and the threshold, the filter algorithm and the wrapper algorithm are combined to form an algorithm called MPMDIWOA. In MPMDIWOA, the filter algorithm and wrapper algorithm are called different times according to changes in MVWC and threshold. Finally, the optimal classification accuracy was found. The proposed method is tested on 10 benchmark datasets from UCI machine learning databases. The experimental results show that the classification accuracy of the proposed algorithm is significantly higher than that of the other three wrapper algorithms and one hybrid algorithm. |
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| AbstractList | Feature selection enhances classification accuracy by removing irrelevant and redundant feature. Feature selection plays an important role in data mining and pattern recognition. In this paper, we propose a hybrid feature subset selection algorithm called the maximum Pearson maximum distance improved whale optimization algorithm (MPMDIWOA). First, based on Pearson's correlation coefficient and correlation distance, a filter algorithm is proposed named maximum Pearson maximum distance (MPMD). Two parameters are proposed in MPMD to adjust the weights of the relevance and redundancy. Second, the modified whale optimization algorithm can act as a wrapper algorithm. After introducing the maximum value without change (MVWC) and the threshold, the filter algorithm and the wrapper algorithm are combined to form an algorithm called MPMDIWOA. In MPMDIWOA, the filter algorithm and wrapper algorithm are called different times according to changes in MVWC and threshold. Finally, the optimal classification accuracy was found. The proposed method is tested on 10 benchmark datasets from UCI machine learning databases. The experimental results show that the classification accuracy of the proposed algorithm is significantly higher than that of the other three wrapper algorithms and one hybrid algorithm. |
| Author | Fan, Jiahao Li, Ying Cui, Xueting Zheng, Yuefeng Xu, Qian Wang, Gang Chen, Yupeng |
| Author_xml | – sequence: 1 givenname: Yuefeng orcidid: 0000-0002-5764-6887 surname: Zheng fullname: Zheng, Yuefeng organization: College of Computer Science and Technology, Jilin University, Changchun, China – sequence: 2 givenname: Ying surname: Li fullname: Li, Ying organization: College of Computer Science and Technology, Jilin University, Changchun, China – sequence: 3 givenname: Gang surname: Wang fullname: Wang, Gang email: wanggang.jlu@gmail.com organization: College of Computer Science and Technology, Jilin University, Changchun, China – sequence: 4 givenname: Yupeng orcidid: 0000-0002-6222-9981 surname: Chen fullname: Chen, Yupeng organization: College of Computer Science and Technology, Jilin University, Changchun, China – sequence: 5 givenname: Qian surname: Xu fullname: Xu, Qian organization: College of Computer Science and Technology, Jilin University, Changchun, China – sequence: 6 givenname: Jiahao surname: Fan fullname: Fan, Jiahao organization: College of Computer Science and Technology, Jilin University, Changchun, China – sequence: 7 givenname: Xueting surname: Cui fullname: Cui, Xueting organization: College of Computer Science and Technology, Jilin University, Changchun, China |
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| SubjectTerms | Accuracy Algorithms Classification Classification algorithms Correlation coefficients Data mining Feature extraction Feature selection filter Filtering algorithms Machine learning Machine learning algorithms MPMD Optimization Optimization algorithms Pattern recognition Redundancy threshold Whales WOA |
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| Title | A Novel Hybrid Algorithm for Feature Selection Based on Whale Optimization Algorithm |
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