Feature selection based on multimodal multi-objective particle swarm optimization and prior information
Due to the conflicting objectives of classification accuracy and selected features size, feature selection is typically approached as a multi-objective optimization problem. However, traditional methods often overlook the inherent multimodal nature of feature selection. Additionally, these methods m...
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| Published in: | Pattern analysis and applications : PAA Vol. 28; no. 4; p. 181 |
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| Format: | Journal Article |
| Language: | English |
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01.12.2025
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| Abstract | Due to the conflicting objectives of classification accuracy and selected features size, feature selection is typically approached as a multi-objective optimization problem. However, traditional methods often overlook the inherent multimodal nature of feature selection. Additionally, these methods might ignore the importance of filter-based prior knowledge in forming equivalent feature subsets, weakening the ability to search for such subsets. An improved feature selection algorithm, named NRMOPSO, is proposed in this study, which is based on multimodal multi-objective particle swarm optimization and integrates a niche method with ReliefF. Initially, the Incrementally Expanding Niche Strategy (IENS) adjusts niche size for comprehensive initial exploration. Subsequently, the ReliefF algorithm evaluates feature importance, incorporating ReliefF-based prior information into the particle search to include significant unselected features while retaining essential ones. Experimental results on 14 UCI datasets indicate that the proposed algorithm effectively identifies multiple equivalent feature subsets and, on high-dimensional datasets, achieves smaller feature subsets without compromising classification accuracy when compared with five classical and advanced multimodal multi-objective optimization algorithms. |
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| AbstractList | Due to the conflicting objectives of classification accuracy and selected features size, feature selection is typically approached as a multi-objective optimization problem. However, traditional methods often overlook the inherent multimodal nature of feature selection. Additionally, these methods might ignore the importance of filter-based prior knowledge in forming equivalent feature subsets, weakening the ability to search for such subsets. An improved feature selection algorithm, named NRMOPSO, is proposed in this study, which is based on multimodal multi-objective particle swarm optimization and integrates a niche method with ReliefF. Initially, the Incrementally Expanding Niche Strategy (IENS) adjusts niche size for comprehensive initial exploration. Subsequently, the ReliefF algorithm evaluates feature importance, incorporating ReliefF-based prior information into the particle search to include significant unselected features while retaining essential ones. Experimental results on 14 UCI datasets indicate that the proposed algorithm effectively identifies multiple equivalent feature subsets and, on high-dimensional datasets, achieves smaller feature subsets without compromising classification accuracy when compared with five classical and advanced multimodal multi-objective optimization algorithms. |
| ArticleNumber | 181 |
| Author | Han, Fei Liu, Wenkai Ling, Qinghua Han, Henry Shi, Jinlong |
| Author_xml | – sequence: 1 givenname: Wenkai surname: Liu fullname: Liu, Wenkai organization: School of Computer Science, Jiangsu University of Science and Technology – sequence: 2 givenname: Qinghua surname: Ling fullname: Ling, Qinghua email: jsjxy_lqh@just.edu.cn organization: School of Computer Science, Jiangsu University of Science and Technology – sequence: 3 givenname: Fei surname: Han fullname: Han, Fei organization: School of Computer Science and Communication Engineering, Jiangsu University – sequence: 4 givenname: Henry surname: Han fullname: Han, Henry organization: Department of Computer Science, Rogers School of Engineering and Computer Science, Baylor University – sequence: 5 givenname: Jinlong surname: Shi fullname: Shi, Jinlong organization: School of Computer Science, Jiangsu University of Science and Technology |
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| Cites_doi | 10.1007/s00500-016-2128-8 10.1016/j.eswa.2021.115620 10.1016/j.asoc.2023.109987 10.1109/TCBB.2015.2476796 10.1007/s11063-024-11553-9 10.1109/SPAC49953.2019.237884 10.1109/TELFOR56187.2022.9983668 10.1109/TEVC.2022.3168052 10.1016/j.eswa.2020.114444 10.1109/TEVC.2023.3292527 10.1109/ZINC55034.2022.9840700 10.1016/j.inffus.2023.102150 10.1007/978-3-540-31880-4_35 10.1109/TEVC.2017.2743016 10.1016/j.asoc.2019.105886 10.1145/1527125.1527138 10.1016/j.eswa.2018.07.013 10.1109/TEVC.2017.2754271 10.1109/TEVC.2015.2504420 10.1109/TCYB.2020.3042243 10.1016/0167-8655(94)90127-9 10.1109/ICARM54641.2022.9959479 10.1016/j.knosys.2023.110640 10.1016/j.asoc.2021.108381 10.1016/j.patcog.2009.06.009 10.1109/ICMSS.2009.5302726 10.1016/j.swevo.2021.100849 10.1016/j.ins.2019.01.084 10.2478/cait-2019-0001 10.1016/j.swevo.2021.100847 10.1016/j.eswa.2023.122707 10.1016/j.ins.2023.03.144 10.1023/A:1025667309714 10.1109/CEC55065.2022.9870227 10.1109/TEVC.2024.3373802 10.1109/TSMCB.2012.2227469 10.1080/03772063.2021.1962747 10.1016/j.asoc.2021.107887 10.1007/s10489-022-03465-9 10.1109/TCYB.2020.3015756 10.1016/j.knosys.2023.110361 10.1109/CEC.2016.7744093 10.1109/CIBCB.2017.8058550 |
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| Keywords | Feature selection Multimodal multiobjective optimization Niching technique Particle swarm optimization ReliefF |
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| References | B Qu (1556_CR17) 2020; 86 R Jiao (1556_CR15) 2023 1556_CR40 Y Hu (1556_CR12) 2023; 635 1556_CR41 M Robnik-Ikonja (1556_CR36) 2003; 53 1556_CR43 C Yue (1556_CR46) 2021; 62 M Shafipour (1556_CR26) 2021; 185 T Yin (1556_CR5) 2024; 104 1556_CR45 1556_CR48 IA Gheyas (1556_CR6) 2010; 43 Z Zhuang (1556_CR13) 2023; 275 X-M Hu (1556_CR28) 2021; 113 1556_CR29 F Han (1556_CR14) 2021; 62 E Zitzler (1556_CR47) 2001; 103 Y Xue (1556_CR11) 2023; 134 X Zhuang (1556_CR9) 2024; 241 R Jiao (1556_CR19) 2024 HB Nguyen (1556_CR23) 2016; 20 P Wang (1556_CR33) 2022; 27 Y Zhang (1556_CR24) 2017; 14 M Amoozegar (1556_CR25) 2018; 113 1556_CR31 1556_CR10 1556_CR35 R Ramaswamy (1556_CR2) 2023; 69 P Pudil (1556_CR7) 1994; 15 F Han (1556_CR27) 2023; 53 1556_CR37 B Xue (1556_CR22) 2013; 43 1556_CR39 S Agrawal (1556_CR32) 2023; 265 1556_CR38 Y Li (1556_CR16) 2019; 494 B Qu (1556_CR18) 2022; 117 1556_CR4 B Xue (1556_CR1) 2016; 20 AU Haq (1556_CR3) 2021; 168 F Han (1556_CR20) 2024; 56 Y Hu (1556_CR34) 2021; 51 C Yue (1556_CR30) 2018; 22 K Chen (1556_CR21) 2022; 52 B Venkatesh (1556_CR8) 2019; 19 Y Zhang (1556_CR44) 2017; 14 Q Yang (1556_CR42) 2018; 22 |
| References_xml | – volume: 20 start-page: 3927 issue: 10 year: 2016 ident: 1556_CR23 publication-title: Soft Comput doi: 10.1007/s00500-016-2128-8 – volume: 185 start-page: 115620 year: 2021 ident: 1556_CR26 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2021.115620 – volume: 134 start-page: 109987 year: 2023 ident: 1556_CR11 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2023.109987 – volume: 14 start-page: 64 issue: 1 year: 2017 ident: 1556_CR24 publication-title: IEEE/ACM Trans Comput Biol Bioinf doi: 10.1109/TCBB.2015.2476796 – volume: 56 start-page: 110 issue: 2 year: 2024 ident: 1556_CR20 publication-title: Neural Process Lett doi: 10.1007/s11063-024-11553-9 – ident: 1556_CR40 doi: 10.1109/SPAC49953.2019.237884 – ident: 1556_CR10 doi: 10.1109/TELFOR56187.2022.9983668 – volume: 27 start-page: 296 issue: 2 year: 2022 ident: 1556_CR33 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2022.3168052 – volume: 168 start-page: 114444 year: 2021 ident: 1556_CR3 publication-title: Exp Syst Appl doi: 10.1016/j.eswa.2020.114444 – year: 2023 ident: 1556_CR15 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2023.3292527 – ident: 1556_CR4 doi: 10.1109/ZINC55034.2022.9840700 – volume: 103 start-page: 21 year: 2001 ident: 1556_CR47 publication-title: TIK Rep – volume: 104 start-page: 102150 year: 2024 ident: 1556_CR5 publication-title: Inf Fusion doi: 10.1016/j.inffus.2023.102150 – ident: 1556_CR39 doi: 10.1007/978-3-540-31880-4_35 – volume: 22 start-page: 578 issue: 4 year: 2018 ident: 1556_CR42 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2017.2743016 – volume: 86 start-page: 105886 year: 2020 ident: 1556_CR17 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2019.105886 – ident: 1556_CR38 – ident: 1556_CR48 doi: 10.1145/1527125.1527138 – volume: 113 start-page: 499 year: 2018 ident: 1556_CR25 publication-title: Exp Syst Appl doi: 10.1016/j.eswa.2018.07.013 – volume: 22 start-page: 805 issue: 5 year: 2018 ident: 1556_CR30 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2017.2754271 – volume: 20 start-page: 606 issue: 4 year: 2016 ident: 1556_CR1 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2015.2504420 – volume: 52 start-page: 7172 issue: 7 year: 2022 ident: 1556_CR21 publication-title: IEEE Trans Cybern doi: 10.1109/TCYB.2020.3042243 – volume: 15 start-page: 1119 issue: 11 year: 1994 ident: 1556_CR7 publication-title: Pattern Recogn Lett doi: 10.1016/0167-8655(94)90127-9 – ident: 1556_CR37 – ident: 1556_CR29 doi: 10.1109/ICARM54641.2022.9959479 – volume: 275 start-page: 110640 year: 2023 ident: 1556_CR13 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2023.110640 – volume: 117 start-page: 108381 year: 2022 ident: 1556_CR18 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2021.108381 – volume: 43 start-page: 5 issue: 1 year: 2010 ident: 1556_CR6 publication-title: Pattern Recogn doi: 10.1016/j.patcog.2009.06.009 – ident: 1556_CR43 doi: 10.1109/ICMSS.2009.5302726 – volume: 62 start-page: 100849 year: 2021 ident: 1556_CR46 publication-title: Swarm Evol Comput doi: 10.1016/j.swevo.2021.100849 – volume: 494 start-page: 233 year: 2019 ident: 1556_CR16 publication-title: Inf Sci doi: 10.1016/j.ins.2019.01.084 – volume: 19 start-page: 3 issue: 1 year: 2019 ident: 1556_CR8 publication-title: Cybern Inf Technol doi: 10.2478/cait-2019-0001 – volume: 62 start-page: 100847 year: 2021 ident: 1556_CR14 publication-title: Swarm Evol Comput doi: 10.1016/j.swevo.2021.100847 – volume: 241 start-page: 122707 year: 2024 ident: 1556_CR9 publication-title: Exp Syst Appl doi: 10.1016/j.eswa.2023.122707 – volume: 635 start-page: 279 year: 2023 ident: 1556_CR12 publication-title: Inf Sci doi: 10.1016/j.ins.2023.03.144 – volume: 53 start-page: 23 issue: 1/2 year: 2003 ident: 1556_CR36 publication-title: Mach Learn doi: 10.1023/A:1025667309714 – ident: 1556_CR31 doi: 10.1109/CEC55065.2022.9870227 – year: 2024 ident: 1556_CR19 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2024.3373802 – volume: 43 start-page: 1656 issue: 6 year: 2013 ident: 1556_CR22 publication-title: IEEE Trans Cybern doi: 10.1109/TSMCB.2012.2227469 – volume: 69 start-page: 9 issue: 1 year: 2023 ident: 1556_CR2 publication-title: IETE J Res doi: 10.1080/03772063.2021.1962747 – volume: 113 start-page: 107887 year: 2021 ident: 1556_CR28 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2021.107887 – volume: 53 start-page: 3545 issue: 3 year: 2023 ident: 1556_CR27 publication-title: Appl Intell doi: 10.1007/s10489-022-03465-9 – volume: 51 start-page: 874 issue: 2 year: 2021 ident: 1556_CR34 publication-title: IEEE Trans Cybern doi: 10.1109/TCYB.2020.3015756 – ident: 1556_CR45 – volume: 265 start-page: 110361 year: 2023 ident: 1556_CR32 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2023.110361 – ident: 1556_CR35 doi: 10.1109/CEC.2016.7744093 – ident: 1556_CR41 doi: 10.1109/CIBCB.2017.8058550 – volume: 14 start-page: 64 issue: 1 year: 2017 ident: 1556_CR44 publication-title: IEEE/ACM Trans Comput Biol Bioinf doi: 10.1109/TCBB.2015.2476796 |
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| SubjectTerms | Accuracy Algorithms Archives & records Artificial intelligence Classification Computer Science Datasets Equivalence Feature selection Genetic algorithms Machine learning Methods Multiple objective analysis Mutation Optimization Original Article Particle swarm optimization Pattern Recognition |
| Title | Feature selection based on multimodal multi-objective particle swarm optimization and prior information |
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