A feature selection algorithm based on redundancy analysis and interaction weight
The performance of some three-dimensional mutual information-based algorithms can be affected, since only relevance and interaction are considered. Aiming at solving the problem, a feature selection algorithm based on redundancy analysis and interaction weight is proposed in this paper. The proposed...
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| Published in | Applied intelligence (Dordrecht, Netherlands) Vol. 51; no. 4; pp. 2672 - 2686 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.04.2021
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0924-669X 1573-7497 |
| DOI | 10.1007/s10489-020-01936-5 |
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| Abstract | The performance of some three-dimensional mutual information-based algorithms can be affected, since only relevance and interaction are considered. Aiming at solving the problem, a feature selection algorithm based on redundancy analysis and interaction weight is proposed in this paper. The proposed algorithm adopts three-way interaction information to measure the interaction among the class label and features, and processes features for interaction weight analysis. Then, it employs symmetric uncertainty to measure the relevance between features and the class label as well as the redundancy between features, and selects the features with greater relevance and interaction as well as smaller redundancy. To validate the performance, the proposed algorithm is compared with several feature selection algorithms. Since relevance, redundancy, and interaction analysis are all presented, the proposed algorithm can obtain better feature selection performance. |
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| AbstractList | The performance of some three-dimensional mutual information-based algorithms can be affected, since only relevance and interaction are considered. Aiming at solving the problem, a feature selection algorithm based on redundancy analysis and interaction weight is proposed in this paper. The proposed algorithm adopts three-way interaction information to measure the interaction among the class label and features, and processes features for interaction weight analysis. Then, it employs symmetric uncertainty to measure the relevance between features and the class label as well as the redundancy between features, and selects the features with greater relevance and interaction as well as smaller redundancy. To validate the performance, the proposed algorithm is compared with several feature selection algorithms. Since relevance, redundancy, and interaction analysis are all presented, the proposed algorithm can obtain better feature selection performance. |
| Author | Guo, Jichang Xiao, Lijun Gu, Xiangyuan Li, Chongyi |
| Author_xml | – sequence: 1 givenname: Xiangyuan surname: Gu fullname: Gu, Xiangyuan organization: School of Electrical and Information Engineering, Tianjin University – sequence: 2 givenname: Jichang surname: Guo fullname: Guo, Jichang email: jcguo@tju.edu.cn organization: School of Electrical and Information Engineering, Tianjin University – sequence: 3 givenname: Chongyi surname: Li fullname: Li, Chongyi organization: School of Electrical and Information Engineering, Tianjin University – sequence: 4 givenname: Lijun surname: Xiao fullname: Xiao, Lijun organization: School of Electrical and Information Engineering, Tianjin University |
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| CitedBy_id | crossref_primary_10_1016_j_knosys_2022_109523 crossref_primary_10_1016_j_jfca_2021_104248 crossref_primary_10_3233_JIFS_224474 crossref_primary_10_1007_s11042_023_15821_z crossref_primary_10_1016_j_patcog_2022_109254 crossref_primary_10_1109_TFUZZ_2022_3169625 crossref_primary_10_3389_fpls_2022_839044 crossref_primary_10_1007_s10489_022_03922_5 crossref_primary_10_1016_j_eswa_2022_117923 crossref_primary_10_1016_j_sigpro_2023_109133 crossref_primary_10_1016_j_eswa_2023_120455 crossref_primary_10_1016_j_asoc_2023_110319 crossref_primary_10_1016_j_asoc_2025_112804 crossref_primary_10_1007_s10489_024_06026_4 |
| Cites_doi | 10.1016/j.knosys.2013.09.019 10.1016/j.patrec.2018.06.005 10.1007/s11063-019-10144-3 10.1145/3136625 10.1109/TKDE.2017.2650906 10.1109/TGRS.2014.2324971 10.1145/1656274.1656278 10.1109/TCYB.2015.2415032 10.1109/TNN.2008.2005601 10.1016/j.eswa.2015.07.007 10.1007/s11042-019-7285-1 10.1016/j.patcog.2018.02.020 10.1007/s10489-017-1010-4 10.1016/j.patcog.2015.02.025 10.1007/s10489-017-0992-2 10.1016/j.knosys.2012.10.001 10.1109/TPAMI.2005.159 10.1109/TKDE.2016.2563436 10.1109/72.298224 10.1016/j.eswa.2011.07.048 10.1007/s00500-019-03910-x 10.1145/1015330.1015377 |
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| Keywords | Feature selection Three-way interaction information Symmetric uncertainty Redundancy analysis |
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| References | Li, Cheng, Wang, Morstatter, Trevino, Tang, Liu (CR22) 2018; 50 Gu, Guo, Wei, He (CR7) 2020; 24 Zhang, Chan, Biggio, Yeung, Roli (CR8) 2016; 46 Wang, Feng, Zhu (CR3) 2018; 48 Gu, Guo (CR6) 2019; 78 Estevez, Tesmer, Perez, Zurada (CR14) 2009; 20 CR16 Fei, Kraus, Zoubir (CR9) 2015; 53 Wang, Wei, Yang, Wang (CR18) 2017; 29 Gao, Hu, Zhang, He (CR19) 2018; 112 Huang, Zhang, Wang, Li, Zhang (CR2) 2018; 48 Zeng, Zhang, Zhang, Yin (CR13) 2015; 48 Sun, Liu, Xu, Chen, Han, Wang (CR12) 2013; 37 Shang, Li, Feng, Jiang, Fan (CR5) 2013; 54 Foithong, Pinngern, Attachoo (CR15) 2012; 39 Gu, Guo, Xiao, Ming, Li (CR26) 2020; 51 CR25 CR23 Gao, Hu, Zhang (CR20) 2018; 79 CR21 Guyon, Elisseeff (CR1) 2003; 3 Peng, Long, Ding (CR11) 2005; 27 Battiti (CR10) 1994; 5 Bennasar, Hicks, Setchi (CR17) 2015; 42 Tang, Kay, He (CR4) 2016; 28 Hall, Frank, Holmes, Pfahringer, Reutemann, Witten (CR24) 2009; 11 WF Gao (1936_CR20) 2018; 79 YW Wang (1936_CR3) 2018; 48 I Guyon (1936_CR1) 2003; 3 JD Li (1936_CR22) 2018; 50 B Tang (1936_CR4) 2016; 28 XY Gu (1936_CR7) 2020; 24 1936_CR16 HC Peng (1936_CR11) 2005; 27 M Bennasar (1936_CR17) 2015; 42 PA Estevez (1936_CR14) 2009; 20 WF Gao (1936_CR19) 2018; 112 T Fei (1936_CR9) 2015; 53 ZL Zeng (1936_CR13) 2015; 48 F Zhang (1936_CR8) 2016; 46 R Battiti (1936_CR10) 1994; 5 X Sun (1936_CR12) 2013; 37 MA Hall (1936_CR24) 2009; 11 XJ Huang (1936_CR2) 2018; 48 XY Gu (1936_CR26) 2020; 51 J Wang (1936_CR18) 2017; 29 CX Shang (1936_CR5) 2013; 54 1936_CR21 XY Gu (1936_CR6) 2019; 78 S Foithong (1936_CR15) 2012; 39 1936_CR25 1936_CR23 |
| References_xml | – volume: 54 start-page: 298 year: 2013 end-page: 309 ident: CR5 article-title: Feature selection via maximizing global information gain for text classification publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2013.09.019 – volume: 112 start-page: 70 year: 2018 end-page: 74 ident: CR19 article-title: Feature selection considering the composition of feature relevancy publication-title: Pattern Recogn Lett doi: 10.1016/j.patrec.2018.06.005 – volume: 51 start-page: 1237 issue: 2 year: 2020 end-page: 1263 ident: CR26 article-title: A feature selection algorithm based on equal interval division and minimal-redundancy-maximal-relevance publication-title: neural process lett doi: 10.1007/s11063-019-10144-3 – volume: 50 start-page: 1 issue: 6 year: 2018 end-page: 45 ident: CR22 article-title: Feature selection: a data perspective publication-title: ACM Comput Surv doi: 10.1145/3136625 – ident: CR16 – volume: 29 start-page: 828 issue: 4 year: 2017 end-page: 841 ident: CR18 article-title: Feature selection by maximizing independent classification information publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2017.2650906 – volume: 53 start-page: 505 issue: 1 year: 2015 end-page: 518 ident: CR9 article-title: Contributions to automatic target recognition systems for underwater mine classification publication-title: IEEE Trans Geosci Remote Sens doi: 10.1109/TGRS.2014.2324971 – volume: 11 start-page: 10 issue: 1 year: 2009 end-page: 18 ident: CR24 article-title: The WEKA data mining software: an update publication-title: SIGKDD Explorations doi: 10.1145/1656274.1656278 – volume: 46 start-page: 766 issue: 3 year: 2016 end-page: 777 ident: CR8 article-title: Adversarial feature selection against evasion attacks publication-title: IEEE Trans Cybern doi: 10.1109/TCYB.2015.2415032 – volume: 20 start-page: 189 issue: 2 year: 2009 end-page: 201 ident: CR14 article-title: Normalized mutual information feature selection publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2008.2005601 – ident: CR25 – volume: 42 start-page: 8520 issue: 22 year: 2015 end-page: 8532 ident: CR17 article-title: Feature selection using joint mutual information maximisation publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2015.07.007 – ident: CR23 – volume: 78 start-page: 19681 issue: 14 year: 2019 end-page: 19695 ident: CR6 article-title: A study on subtractive pixel adjacency matrix features publication-title: Multimed Tools Appl doi: 10.1007/s11042-019-7285-1 – ident: CR21 – volume: 79 start-page: 328 year: 2018 end-page: 339 ident: CR20 article-title: Class-specific mutual information variation for feature selection publication-title: Pattern Recogn doi: 10.1016/j.patcog.2018.02.020 – volume: 3 start-page: 1157 year: 2003 end-page: 1182 ident: CR1 article-title: An introduction to variable and feature selection publication-title: J Mach Learn Res – volume: 48 start-page: 868 issue: 4 year: 2018 end-page: 885 ident: CR3 article-title: Novel artificial bee colony based feature selection method for filtering redundant information publication-title: Appl Intell doi: 10.1007/s10489-017-1010-4 – volume: 48 start-page: 2656 issue: 8 year: 2015 end-page: 2666 ident: CR13 article-title: A novel feature selection method considering feature interaction publication-title: Pattern Recogn doi: 10.1016/j.patcog.2015.02.025 – volume: 48 start-page: 594 issue: 3 year: 2018 end-page: 607 ident: CR2 article-title: Feature clustering based support vector machine recursive feature elimination for gene selection publication-title: Appl Intell doi: 10.1007/s10489-017-0992-2 – volume: 37 start-page: 541 year: 2013 end-page: 549 ident: CR12 article-title: Feature selection using dynamic weights for classification publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2012.10.001 – volume: 27 start-page: 1226 issue: 8 year: 2005 end-page: 1238 ident: CR11 article-title: Feature selection based on mutual information: criteria of max-dependency, max-relevance and min-redundancy publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2005.159 – volume: 28 start-page: 2508 issue: 9 year: 2016 end-page: 2521 ident: CR4 article-title: Toward optimal feature selection in naive bayes for text categorization publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2016.2563436 – volume: 5 start-page: 537 issue: 4 year: 1994 end-page: 550 ident: CR10 article-title: Using mutual information for selecting features in supervised neural net learning publication-title: IEEE Trans Neural Netw doi: 10.1109/72.298224 – volume: 39 start-page: 574 issue: 1 year: 2012 end-page: 584 ident: CR15 article-title: Feature subset selection wrapper based on mutual information and rough sets publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2011.07.048 – volume: 24 start-page: 333 issue: 1 year: 2020 end-page: 340 ident: CR7 article-title: Spatial-domain steganalytic feature selection based on three-way interaction information and KS test publication-title: Soft Comput doi: 10.1007/s00500-019-03910-x – volume: 20 start-page: 189 issue: 2 year: 2009 ident: 1936_CR14 publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2008.2005601 – volume: 5 start-page: 537 issue: 4 year: 1994 ident: 1936_CR10 publication-title: IEEE Trans Neural Netw doi: 10.1109/72.298224 – volume: 28 start-page: 2508 issue: 9 year: 2016 ident: 1936_CR4 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2016.2563436 – volume: 42 start-page: 8520 issue: 22 year: 2015 ident: 1936_CR17 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2015.07.007 – volume: 54 start-page: 298 year: 2013 ident: 1936_CR5 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2013.09.019 – volume: 46 start-page: 766 issue: 3 year: 2016 ident: 1936_CR8 publication-title: IEEE Trans Cybern doi: 10.1109/TCYB.2015.2415032 – ident: 1936_CR23 – volume: 48 start-page: 2656 issue: 8 year: 2015 ident: 1936_CR13 publication-title: Pattern Recogn doi: 10.1016/j.patcog.2015.02.025 – ident: 1936_CR21 – volume: 48 start-page: 594 issue: 3 year: 2018 ident: 1936_CR2 publication-title: Appl Intell doi: 10.1007/s10489-017-0992-2 – volume: 53 start-page: 505 issue: 1 year: 2015 ident: 1936_CR9 publication-title: IEEE Trans Geosci Remote Sens doi: 10.1109/TGRS.2014.2324971 – volume: 11 start-page: 10 issue: 1 year: 2009 ident: 1936_CR24 publication-title: SIGKDD Explorations doi: 10.1145/1656274.1656278 – ident: 1936_CR25 – ident: 1936_CR16 doi: 10.1145/1015330.1015377 – volume: 24 start-page: 333 issue: 1 year: 2020 ident: 1936_CR7 publication-title: Soft Comput doi: 10.1007/s00500-019-03910-x – volume: 112 start-page: 70 year: 2018 ident: 1936_CR19 publication-title: Pattern Recogn Lett doi: 10.1016/j.patrec.2018.06.005 – volume: 50 start-page: 1 issue: 6 year: 2018 ident: 1936_CR22 publication-title: ACM Comput Surv doi: 10.1145/3136625 – volume: 27 start-page: 1226 issue: 8 year: 2005 ident: 1936_CR11 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2005.159 – volume: 48 start-page: 868 issue: 4 year: 2018 ident: 1936_CR3 publication-title: Appl Intell doi: 10.1007/s10489-017-1010-4 – volume: 29 start-page: 828 issue: 4 year: 2017 ident: 1936_CR18 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2017.2650906 – volume: 51 start-page: 1237 issue: 2 year: 2020 ident: 1936_CR26 publication-title: neural process lett doi: 10.1007/s11063-019-10144-3 – volume: 3 start-page: 1157 year: 2003 ident: 1936_CR1 publication-title: J Mach Learn Res – volume: 37 start-page: 541 year: 2013 ident: 1936_CR12 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2012.10.001 – volume: 39 start-page: 574 issue: 1 year: 2012 ident: 1936_CR15 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2011.07.048 – volume: 79 start-page: 328 year: 2018 ident: 1936_CR20 publication-title: Pattern Recogn doi: 10.1016/j.patcog.2018.02.020 – volume: 78 start-page: 19681 issue: 14 year: 2019 ident: 1936_CR6 publication-title: Multimed Tools Appl doi: 10.1007/s11042-019-7285-1 |
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| Title | A feature selection algorithm based on redundancy analysis and interaction weight |
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