Multiobjective optimization algorithm with dynamic operator selection for feature selection in high-dimensional classification
Feature selection (FS) is an important technique in data preprocessing that aims to reduce the number of features for training while maintaining a high accuracy for classification. In recent studies, FS has been extended to optimize multiple objectives simultaneously in classification. To better sol...
Uložené v:
| Vydané v: | Applied soft computing Ročník 143; s. 110360 |
|---|---|
| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier B.V
01.08.2023
|
| Predmet: | |
| ISSN: | 1568-4946 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Feature selection (FS) is an important technique in data preprocessing that aims to reduce the number of features for training while maintaining a high accuracy for classification. In recent studies, FS has been extended to optimize multiple objectives simultaneously in classification. To better solve this problem, this paper proposes a new multiobjective optimization algorithm with dynamic operator selection for feature selection in high-dimensional classification, called FS-DOS. First, two complementary search operators with different characteristics are designed, where the first operator is a quick search (QS) operator aiming to accelerate the convergence speed, and the other operator is a modified binary differential evolution (BDE) operator that can prevent the algorithm from falling into a local optimum. In addition, a dynamic selection strategy based on the idea of resource allocation is also designed to dynamically select the most suitable operator for each solution according to its corresponding performance improvement on aggregated objective values. The simulation results on fifteen different real-world high-dimensional FS datasets show that FS-DOS can obtain a feature subset with higher quality than several state-of-the-art FS algorithms. Importantly, in terms of error rate, FS-DOS wins 55 out of 75 comparisons. In terms of dimensionality reduction, the number of features selected by FS-DOS is between one hundredth and one thousandth of the original dataset.
•This paper proposes an effective evolutionary algorithm with dynamic operator selection strategy for high-dimensional feature selection.•The QS operator is used to select the most important features for accelerating the convergence, and the BDE operator with strong exploration ability is designed to avoid local optimum.•The proposed method presents is superior to state-of-the-art FS methods on 15 real-world medical high-dimensional datasets. |
|---|---|
| AbstractList | Feature selection (FS) is an important technique in data preprocessing that aims to reduce the number of features for training while maintaining a high accuracy for classification. In recent studies, FS has been extended to optimize multiple objectives simultaneously in classification. To better solve this problem, this paper proposes a new multiobjective optimization algorithm with dynamic operator selection for feature selection in high-dimensional classification, called FS-DOS. First, two complementary search operators with different characteristics are designed, where the first operator is a quick search (QS) operator aiming to accelerate the convergence speed, and the other operator is a modified binary differential evolution (BDE) operator that can prevent the algorithm from falling into a local optimum. In addition, a dynamic selection strategy based on the idea of resource allocation is also designed to dynamically select the most suitable operator for each solution according to its corresponding performance improvement on aggregated objective values. The simulation results on fifteen different real-world high-dimensional FS datasets show that FS-DOS can obtain a feature subset with higher quality than several state-of-the-art FS algorithms. Importantly, in terms of error rate, FS-DOS wins 55 out of 75 comparisons. In terms of dimensionality reduction, the number of features selected by FS-DOS is between one hundredth and one thousandth of the original dataset.
•This paper proposes an effective evolutionary algorithm with dynamic operator selection strategy for high-dimensional feature selection.•The QS operator is used to select the most important features for accelerating the convergence, and the BDE operator with strong exploration ability is designed to avoid local optimum.•The proposed method presents is superior to state-of-the-art FS methods on 15 real-world medical high-dimensional datasets. |
| ArticleNumber | 110360 |
| Author | Coello Coello, Carlos A. Wei, Wenhong Xuan, Manlin Li, Lingjie Ming, Zhong Lin, Qiuzhen |
| Author_xml | – sequence: 1 givenname: Wenhong surname: Wei fullname: Wei, Wenhong organization: School of Computer Science and Technology, Dongguan University of Technology, Dongguan, PR China – sequence: 2 givenname: Manlin surname: Xuan fullname: Xuan, Manlin organization: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, PR China – sequence: 3 givenname: Lingjie orcidid: 0000-0002-8289-2211 surname: Li fullname: Li, Lingjie email: vilitejie@qq.com organization: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, PR China – sequence: 4 givenname: Qiuzhen surname: Lin fullname: Lin, Qiuzhen organization: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, PR China – sequence: 5 givenname: Zhong surname: Ming fullname: Ming, Zhong organization: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, PR China – sequence: 6 givenname: Carlos A. surname: Coello Coello fullname: Coello Coello, Carlos A. organization: CINVESTAV-IPN, Department of Computer Science, Mexico, D.F. 07360, Mexico |
| BookMark | eNp9kE9PwyAYhznMxG36BTz1C7QCbWmbeDGL_5IZL3omlML6Nm1ZgM3Mg59dunkwHnaB8MvvgZdngWajGRVCNwQnBBN22yXCGZlQTNOEEJwyPENzkrMyzqqMXaKFcx0OxYqWc_T9uus9mLpT0sNeRWbrYYAvEbIxEv3GWPDtEH2GNWoOoxhAho6ywhsbOdVPWGjqcNJK-J1Vf1IYoxY2bdzAoEYXEtFHshfOgQZ5fOIKXWjRO3X9uy_Rx-PD--o5Xr89vazu17FMMfYxy-oiD9OXZd6wXBZZpglVumYUF1SUrEobQfKK1FIWqSa1prIoG51nuKYkpUW6RPR0r7TGOas031oYhD1wgvlkjXd8ssYna_xkLUDlP0iCP47trYD-PHp3QlX41B6U5U6CGqVqwAY5vDFwDv8B9QuQIA |
| CitedBy_id | crossref_primary_10_1016_j_matcom_2025_01_007 crossref_primary_10_1016_j_eswa_2024_125300 crossref_primary_10_1016_j_eswa_2025_127227 crossref_primary_10_1007_s12530_024_09595_4 crossref_primary_10_1016_j_swevo_2023_101360 crossref_primary_10_1007_s40314_025_03408_3 crossref_primary_10_1016_j_asoc_2023_111141 crossref_primary_10_1007_s13042_024_02107_5 crossref_primary_10_1016_j_cor_2024_106821 crossref_primary_10_1016_j_asoc_2025_113698 crossref_primary_10_1016_j_ins_2024_120483 crossref_primary_10_1016_j_swevo_2025_101915 crossref_primary_10_1016_j_swevo_2024_101715 crossref_primary_10_1007_s11063_024_11440_3 crossref_primary_10_1016_j_buildenv_2024_111185 crossref_primary_10_1109_TPAMI_2024_3416196 |
| Cites_doi | 10.1109/TIP.2017.2733200 10.1016/j.eswa.2016.06.004 10.1109/TEVC.2019.2913831 10.1016/j.asoc.2023.110031 10.1109/MCI.2017.2708578 10.1016/j.asoc.2019.105957 10.1109/TCYB.2016.2586191 10.1016/j.compbiolchem.2007.09.005 10.1016/j.asoc.2021.108297 10.1016/j.asoc.2017.06.021 10.1016/j.eswa.2022.119080 10.1109/TEVC.2007.892759 10.1145/1656274.1656278 10.1016/j.ins.2019.08.040 10.1016/j.asoc.2023.109987 10.1162/evco_a_00269 10.1016/j.neucom.2020.02.028 10.1109/TCBB.2015.2476796 10.1016/j.asoc.2020.106255 10.1016/j.ins.2020.08.083 10.1109/TIT.1963.1057810 10.3233/IDA-1997-1302 10.1016/j.ins.2021.02.061 10.1016/j.neucom.2011.03.034 10.1109/TSMCB.2012.2227469 10.1109/TCYB.2021.3049712 10.1109/TEVC.2019.2949841 10.1109/21.97458 10.1016/j.asoc.2018.02.051 10.1109/TEVC.2018.2872453 10.1109/TEVC.2023.3254155 10.1007/s00500-016-2128-8 10.1016/j.asoc.2020.107002 10.1016/j.patcog.2020.107804 10.1109/CIDM.2009.4938668 10.1109/TCYB.2017.2714145 10.1109/T-C.1971.223410 10.1109/TSMC.2016.2605132 10.1109/TKDE.2011.181 10.1109/TCYB.2020.2979930 10.1109/TEVC.2018.2869405 10.1109/TEVC.2013.2281535 10.1109/TCYB.2018.2871673 |
| ContentType | Journal Article |
| Copyright | 2023 Elsevier B.V. |
| Copyright_xml | – notice: 2023 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.asoc.2023.110360 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| ExternalDocumentID | 10_1016_j_asoc_2023_110360 S1568494623003782 |
| GroupedDBID | --K --M .DC .~1 0R~ 1B1 1~. 1~5 23M 4.4 457 4G. 53G 5GY 5VS 6J9 7-5 71M 8P~ AABNK AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AATTM AAXKI AAXUO AAYFN AAYWO ABBOA ABFNM ABFRF ABJNI ABMAC ABWVN ABXDB ACDAQ ACGFO ACGFS ACNNM ACRLP ACRPL ACVFH ACZNC ADBBV ADCNI ADEZE ADJOM ADMUD ADNMO ADTZH AEBSH AECPX AEFWE AEIPS AEKER AENEX AEUPX AFJKZ AFPUW AFTJW AFXIZ AGCQF AGHFR AGQPQ AGRNS AGUBO AGYEJ AHJVU AHZHX AIALX AIEXJ AIGII AIIUN AIKHN AITUG AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU AOUOD APXCP ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC BNPGV CS3 EBS EFJIC EFKBS EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HVGLF HZ~ IHE J1W JJJVA KOM M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SDF SDG SES SEW SPC SPCBC SST SSV SSZ T5K UHS UNMZH ~G- 9DU AAYXX ACLOT CITATION EFLBG ~HD |
| ID | FETCH-LOGICAL-c300t-64b75494885d65c744f12efb62072a8693da1591bcc73f1bf2c78df540b213273 |
| ISICitedReferencesCount | 20 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001053604200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1568-4946 |
| IngestDate | Tue Nov 18 21:40:51 EST 2025 Sat Nov 29 06:56:33 EST 2025 Sat Aug 09 17:32:21 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Feature selection High-dimensional classification Multiobjective optimization Evolutionary algorithm Resource allocation |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c300t-64b75494885d65c744f12efb62072a8693da1591bcc73f1bf2c78df540b213273 |
| ORCID | 0000-0002-8289-2211 |
| ParticipantIDs | crossref_primary_10_1016_j_asoc_2023_110360 crossref_citationtrail_10_1016_j_asoc_2023_110360 elsevier_sciencedirect_doi_10_1016_j_asoc_2023_110360 |
| PublicationCentury | 2000 |
| PublicationDate | August 2023 2023-08-00 |
| PublicationDateYYYYMMDD | 2023-08-01 |
| PublicationDate_xml | – month: 08 year: 2023 text: August 2023 |
| PublicationDecade | 2020 |
| PublicationTitle | Applied soft computing |
| PublicationYear | 2023 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Zhang, Li (b37) 2007; 11 Zhou, Zhang, Kang, Zhang, Wang (b26) 2021; 547 Safavian, Landgrebe (b13) 1991; 21 Zhang, Gong, Cheng (b18) 2017; 14 Bao, Gao, Gu, Xu, Goodman (b22) 2023; 213 Cheng, Chu, Xu, Zhang (b25) 2021 Zhang, Duan, Zhang, Cheng, Jin, Tang (b33) 2017; 12 Cheng, Chen, Qiu, Zhang (b32) 2020; 394 Xue, Zhu, Neri (b48) 2023; 134 Wang, Ong, Sun, Gupta, Zhang (b35) 2019; 23 U. Fayyad, K. Irani, Multi-interval discretization of continuous-valued attributes for classification learning, in: Proc. 13th Int. Joint Conf. Artif. Intell. 2. Chambéry, France, 1993, pp. 1022–1027. Hall (b53) 2009; 11 Maldonado, López (b8) 2018; 67 Na, Han, Ren, Zhong (b4) 2020; 52 Nguyen, Xue, Andreae, Ishibuchi, Zhang (b10) 2020; 24 Armina, Zain, Ali, Sallehuddin (b21) 2017; 892 M. Gutlein, E. Frank, M. Hall, A. Karwath, Large-scale attribute selection using wrappers, in: 2009 IEEE Symposium on Computational Intelligence and Data Mining. Presented at the 2009 IEEE Symposium on Computational Intelligence and Data Mining,, 2009, pp. 332–339 Tanabe, Ishibuchi (b39) 2020; 24 Tarkhaneh, Nguyen, Mazaheri (b44) 2021; 565 Deb, Jain (b20) 2014; 18 Nguyen, Xue, Liu, Andreae, Zhang (b17) 2016; 20 Li, Lin, Ming (b24) 2021; 101 Wang, Zhu (b7) 2018; 48 Chuang, Chang, Tu, Yang (b29) 2008; 32 Xue, Tang, Xu, Liang, Neri (b19) 2021; 6 . Di Martino, Senatore (b2) 2021; 98 Tian, Lu, Zhang, Tan, Jin (b47) 2021; 51 Hua, Zhou, Hua, Zhang (b6) 2020; 87 Wang, Zhen, Deng, Zhang, Li, Yuan, Zeng (b31) 2022; 52 Mokhtia, Eftekhari, Saberi-Movahed (b3) 2020; 91 Kabir, Shahjahan, Murase (b14) 2011; 74 Marill, Green (b12) 1963; 9 Zhang, Zhou, Pan, Zhang, Zeng, Jin (b34) 2020; 50 Zhang, Gong, Gao, Tian, Sun (b45) 2020; 507 Patterson, Zhang (b51) 2007 Song, Zhang, Gong, Gao (b42) 2021 Song, Zhang, Gong, Sun (b43) 2021; 112 Xue, Zhang, Browne (b15) 2013; 43 Zhang, Wang, Gong, Sun (b27) 2021 Dash, Liu (b1) 1997; 1 Gastelum Chavira, Leyva Lopez, Solano Noriega, Ahumada Valenzuela, Alvarez Carrillo (b30) 2017; 60 Li, Yao (b38) 2020; 28 Tran, Xue, Zhang (b9) 2018; 48 Cliff (b54) 2014 Wang, Gong, Li, Gu, Tian (b23) 2022; 116 Yao, Liu, Jiang, Han, Han (b5) 2017; 26 Li, Xuan, Lin, Jiang, Ming, Tan (b50) 2023 Cai, Yang, Fan, Zhang (b40) 2017; 47 Song, Ni, Wang (b41) 2013; 25 Li, Lin, Ming, Wong, Gong, Coello Coello (b36) 2022 Pan, Chen, Xiong (b49) 2023; 135 Whitney (b11) 1971; C-20 Tran, Xue, Zhang (b46) 2019; 23 Zorarpacı, Özel (b16) 2016; 62 Zorarpacı (10.1016/j.asoc.2023.110360_b16) 2016; 62 Wang (10.1016/j.asoc.2023.110360_b35) 2019; 23 Wang (10.1016/j.asoc.2023.110360_b23) 2022; 116 Xue (10.1016/j.asoc.2023.110360_b19) 2021; 6 Xue (10.1016/j.asoc.2023.110360_b15) 2013; 43 Maldonado (10.1016/j.asoc.2023.110360_b8) 2018; 67 Safavian (10.1016/j.asoc.2023.110360_b13) 1991; 21 Di Martino (10.1016/j.asoc.2023.110360_b2) 2021; 98 Tanabe (10.1016/j.asoc.2023.110360_b39) 2020; 24 Song (10.1016/j.asoc.2023.110360_b42) 2021 Wang (10.1016/j.asoc.2023.110360_b7) 2018; 48 Zhou (10.1016/j.asoc.2023.110360_b26) 2021; 547 Deb (10.1016/j.asoc.2023.110360_b20) 2014; 18 Song (10.1016/j.asoc.2023.110360_b43) 2021; 112 Armina (10.1016/j.asoc.2023.110360_b21) 2017; 892 Nguyen (10.1016/j.asoc.2023.110360_b17) 2016; 20 Gastelum Chavira (10.1016/j.asoc.2023.110360_b30) 2017; 60 Li (10.1016/j.asoc.2023.110360_b36) 2022 Patterson (10.1016/j.asoc.2023.110360_b51) 2007 Hall (10.1016/j.asoc.2023.110360_b53) 2009; 11 Zhang (10.1016/j.asoc.2023.110360_b37) 2007; 11 Cliff (10.1016/j.asoc.2023.110360_b54) 2014 Zhang (10.1016/j.asoc.2023.110360_b27) 2021 10.1016/j.asoc.2023.110360_b28 Na (10.1016/j.asoc.2023.110360_b4) 2020; 52 Nguyen (10.1016/j.asoc.2023.110360_b10) 2020; 24 Tian (10.1016/j.asoc.2023.110360_b47) 2021; 51 Yao (10.1016/j.asoc.2023.110360_b5) 2017; 26 Cheng (10.1016/j.asoc.2023.110360_b25) 2021 Li (10.1016/j.asoc.2023.110360_b50) 2023 Marill (10.1016/j.asoc.2023.110360_b12) 1963; 9 Zhang (10.1016/j.asoc.2023.110360_b45) 2020; 507 Mokhtia (10.1016/j.asoc.2023.110360_b3) 2020; 91 Cheng (10.1016/j.asoc.2023.110360_b32) 2020; 394 Kabir (10.1016/j.asoc.2023.110360_b14) 2011; 74 Cai (10.1016/j.asoc.2023.110360_b40) 2017; 47 Zhang (10.1016/j.asoc.2023.110360_b18) 2017; 14 Pan (10.1016/j.asoc.2023.110360_b49) 2023; 135 10.1016/j.asoc.2023.110360_b52 Tran (10.1016/j.asoc.2023.110360_b9) 2018; 48 Song (10.1016/j.asoc.2023.110360_b41) 2013; 25 Zhang (10.1016/j.asoc.2023.110360_b33) 2017; 12 Xue (10.1016/j.asoc.2023.110360_b48) 2023; 134 Wang (10.1016/j.asoc.2023.110360_b31) 2022; 52 Li (10.1016/j.asoc.2023.110360_b24) 2021; 101 Bao (10.1016/j.asoc.2023.110360_b22) 2023; 213 Hua (10.1016/j.asoc.2023.110360_b6) 2020; 87 Li (10.1016/j.asoc.2023.110360_b38) 2020; 28 Tran (10.1016/j.asoc.2023.110360_b46) 2019; 23 Whitney (10.1016/j.asoc.2023.110360_b11) 1971; C-20 Zhang (10.1016/j.asoc.2023.110360_b34) 2020; 50 Tarkhaneh (10.1016/j.asoc.2023.110360_b44) 2021; 565 Chuang (10.1016/j.asoc.2023.110360_b29) 2008; 32 Dash (10.1016/j.asoc.2023.110360_b1) 1997; 1 |
| References_xml | – volume: 134 year: 2023 ident: b48 article-title: A feature selection approach based on NSGA-II with relieff publication-title: Appl. Soft Comput. – reference: M. Gutlein, E. Frank, M. Hall, A. Karwath, Large-scale attribute selection using wrappers, in: 2009 IEEE Symposium on Computational Intelligence and Data Mining. Presented at the 2009 IEEE Symposium on Computational Intelligence and Data Mining,, 2009, pp. 332–339, – start-page: 769 year: 2007 end-page: 775 ident: b51 article-title: Fitness functions in genetic programming for classification with unbalanced data publication-title: AI 2007: Advances in Artificial Intelligence, Lecture Notes in Computer Science – volume: 9 start-page: 11 year: 1963 end-page: 17 ident: b12 article-title: On the effectiveness of receptors in recognition systems publication-title: IEEE Trans. Inform. Theory – volume: 18 start-page: 577 year: 2014 end-page: 601 ident: b20 article-title: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach part I: solving problems with box constraints publication-title: IEEE Trans. Evolut. Comput. – volume: 394 start-page: 70 year: 2020 end-page: 83 ident: b32 article-title: A subregion division based multi-objective evolutionary algorithm for SVM training set selection publication-title: Neurocomputing – volume: 101 year: 2021 ident: b24 article-title: Multi-objective optimization using self-organizing decomposition and its applications to crashworthiness design publication-title: Appl. Soft Comput. – volume: 24 start-page: 170 year: 2020 end-page: 184 ident: b10 article-title: Multiple reference points-based decomposition for multiobjective feature selection in classification: static and dynamic mechanisms publication-title: IEEE Trans. Evol. Comput. – volume: 43 start-page: 1656 year: 2013 end-page: 1671 ident: b15 article-title: Particle swarm optimization for feature selection in classification: a multi-objective approach publication-title: IEEE Trans. Cybern. – volume: 892 year: 2017 ident: b21 article-title: A review on missing value estimation using imputation algorithm publication-title: J. Phys.: Conf. Ser. – year: 2014 ident: b54 article-title: Ordinal Methods for Behavioral Data Analysis – volume: 28 start-page: 227 year: 2020 end-page: 253 ident: b38 article-title: What weights work for you? adapting weights for any pareto front shape in decomposition-based evolutionary multiobjective optimisation publication-title: Evolut. Comput. – reference: U. Fayyad, K. Irani, Multi-interval discretization of continuous-valued attributes for classification learning, in: Proc. 13th Int. Joint Conf. Artif. Intell. 2. Chambéry, France, 1993, pp. 1022–1027. – volume: 112 year: 2021 ident: b43 article-title: Feature selection using bare-bones particle swarm optimization with mutual information publication-title: Pattern Recognit. – volume: 1 start-page: 131 year: 1997 end-page: 156 ident: b1 article-title: Feature selection for classification publication-title: Intell. Data Anal. – volume: 74 start-page: 2914 year: 2011 end-page: 2928 ident: b14 article-title: A new local search based hybrid genetic algorithm for feature selection publication-title: Neurocomputing – volume: 52 start-page: 1 year: 2020 end-page: 11 ident: b4 article-title: Modified BBO-Based multivariate time-series prediction system with feature subset selection and model parameter optimization publication-title: IEEE Trans. Cybern. – start-page: 1 year: 2021 end-page: 14 ident: b25 article-title: A steering-matrix-based multiobjective evolutionary algorithm for high-dimensional feature selection publication-title: IEEE Trans. Cybern. – volume: 48 start-page: 1733 year: 2018 end-page: 1746 ident: b9 article-title: A new representation in PSO for discretization-based feature selection publication-title: IEEE Trans. Cybern. – volume: C-20 start-page: 1100 year: 1971 end-page: 1103 ident: b11 article-title: A direct method of nonparametric measurement selection publication-title: IEEE Trans. Comput. – volume: 67 start-page: 94 year: 2018 end-page: 105 ident: b8 article-title: Dealing with high-dimensional class-imbalanced datasets: embedded feature selection for SVM classification publication-title: Appl. Soft Comput. – volume: 23 start-page: 473 year: 2019 end-page: 487 ident: b46 article-title: Variable-length particle swarm optimization for feature selection on high-dimensional classification publication-title: IEEE Trans. Evol. Comput. – volume: 507 start-page: 67 year: 2020 end-page: 85 ident: b45 article-title: Binary differential evolution with self-learning for multi-objective feature selection publication-title: Inform. Sci. – volume: 60 start-page: 190 year: 2017 end-page: 201 ident: b30 article-title: A credit ranking model for a parafinancial company based on the ELECTRE-III method and a multiobjective evolutionary algorithm publication-title: Appl. Soft Comput. – volume: 52 start-page: 8326 year: 2022 end-page: 8339 ident: b31 article-title: Multiobjective optimization-aided decision-making system for large-scale manufacturing planning publication-title: IEEE Trans. Cybern. – volume: 12 start-page: 43 year: 2017 end-page: 53 ident: b33 article-title: Pattern recommendation in task-oriented applications: a multi-objective perspective [Application notes] publication-title: IEEE Comput. Intell. Mag. – volume: 21 start-page: 660 year: 1991 end-page: 674 ident: b13 article-title: A survey of decision tree classifier methodology publication-title: IEEE Trans. Syst. Man Cybern. – volume: 11 start-page: 10 year: 2009 end-page: 18 ident: b53 article-title: The WEKA data mining software: An update publication-title: CM SIGKDD Explorations Newslett – volume: 565 start-page: 278 year: 2021 end-page: 305 ident: b44 article-title: A novel wrapper-based feature subset selection method using modified binary differential evolution algorithm publication-title: Inform. Sci. – volume: 87 year: 2020 ident: b6 article-title: Strong approximate Markov blanket and its application on filter-based feature selection publication-title: Appl. Soft Comput. – volume: 547 start-page: 841 year: 2021 end-page: 859 ident: b26 article-title: A problem-specific non-dominated sorting genetic algorithm for supervised feature selection publication-title: Inform. Sci. – start-page: 1 year: 2021 ident: b27 article-title: Clustering-guided particle swarm feature selection algorithm for high-dimensional imbalanced data with missing values publication-title: IEEE Trans. Evolut. Comput. – volume: 14 start-page: 64 year: 2017 end-page: 75 ident: b18 article-title: Multi-objective particle swarm optimization approach for cost-based feature selection in classification publication-title: IEEE/ACM Trans. Comput. Biol. Bioinform. – volume: 25 start-page: 1 year: 2013 end-page: 14 ident: b41 article-title: A fast clustering-based feature subset selection algorithm for high-dimensional data publication-title: IEEE Trans. Knowl. Data Eng. – volume: 50 start-page: 703 year: 2020 end-page: 716 ident: b34 article-title: A network reduction-based multiobjective evolutionary algorithm for community detection in large-scale complex networks publication-title: IEEE Trans. Cybern. – volume: 6 start-page: 1 year: 2021 end-page: 10 ident: b19 article-title: Multi-objective feature selection with missing data in classification publication-title: IEEE Trans. Emerg. Top. Comput. Intell. – volume: 98 year: 2021 ident: b2 article-title: Balancing the user-driven feature selection and their incidence in the clustering structure formation publication-title: Appl. Soft Comput. – volume: 20 start-page: 3927 year: 2016 end-page: 3946 ident: b17 article-title: New mechanism for archive maintenance in PSO-based multi-objective feature selection publication-title: Soft Comput – volume: 51 start-page: 3115 year: 2021 end-page: 3128 ident: b47 article-title: Solving large-scale multiobjective optimization problems with sparse optimal solutions via unsupervised neural networks publication-title: IEEE Trans. Cybern. – volume: 62 start-page: 91 year: 2016 end-page: 103 ident: b16 article-title: A hybrid approach of differential evolution and artificial bee colony for feature selection publication-title: Expert Syst. Appl. – volume: 213 year: 2023 ident: b22 article-title: A new adaptive decomsposition-based evolutionary algorithm for multi- and many-objective optimization publication-title: Expert Syst. Appl. – volume: 48 start-page: 329 year: 2018 end-page: 341 ident: b7 article-title: Sparse graph embedding unsupervised feature selection publication-title: IEEE Trans. Syst. Man Cybern. Syst. – volume: 116 year: 2022 ident: b23 article-title: A hypervolume distribution entropy guided computation resource allocation mechanism for the multiobjective evolutionary algorithm based on decomposition publication-title: Appl. Soft Comput. – volume: 135 year: 2023 ident: b49 article-title: A high-dimensional feature selection method based on modified Gray Wolf Optimization publication-title: Appl. Soft Comput. – volume: 91 year: 2020 ident: b3 article-title: Feature selection based on regularization of sparsity based regression models by hesitant fuzzy correlation publication-title: Appl. Soft Comput. – reference: . – year: 2022 ident: b36 article-title: An immune-inspired resources allocation strategy for many-objective optimization publication-title: IEEE Trans. Syst. Man Cybern. Syst. – start-page: 1 year: 2021 end-page: 14 ident: b42 article-title: A fast hybrid feature selection based on correlation-guided clustering and particle swarm optimization for high-dimensional data publication-title: IEEE Trans. Cybern. – year: 2023 ident: b50 article-title: An evolutionary multitasking algorithm with multiple filtering for high-dimensional feature selection publication-title: IEEE Trans. Evolut. Comput. – volume: 11 start-page: 712 year: 2007 end-page: 731 ident: b37 article-title: MOEA/D: a multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Trans. Evol. Comput. – volume: 24 start-page: 720 year: 2020 end-page: 734 ident: b39 article-title: A framework to handle multimodal multiobjective optimization in decomposition-based evolutionary algorithms publication-title: IEEE Trans. Evol. Comput. – volume: 26 start-page: 5257 year: 2017 end-page: 5269 ident: b5 article-title: LLE score: A new filter-based unsupervised feature selection method based on nonlinear manifold embedding and its application to image recognition publication-title: IEEE Trans. Image Process. – volume: 23 start-page: 556 year: 2019 end-page: 571 ident: b35 article-title: A generator for multiobjective test problems with difficult-to-approximate Pareto front boundaries publication-title: IEEE Trans. Evol. Comput. – volume: 32 start-page: 29 year: 2008 end-page: 38 ident: b29 article-title: Improved binary PSO for feature selection using gene expression data publication-title: Comput. Biol. Chem. – volume: 47 start-page: 2824 year: 2017 end-page: 2837 ident: b40 article-title: Decomposition-based-sorting and angle-based-selection for evolutionary multiobjective and many-objective optimization publication-title: IEEE Trans. Cybern. – volume: 26 start-page: 5257 year: 2017 ident: 10.1016/j.asoc.2023.110360_b5 article-title: LLE score: A new filter-based unsupervised feature selection method based on nonlinear manifold embedding and its application to image recognition publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2017.2733200 – volume: 62 start-page: 91 year: 2016 ident: 10.1016/j.asoc.2023.110360_b16 article-title: A hybrid approach of differential evolution and artificial bee colony for feature selection publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2016.06.004 – start-page: 1 year: 2021 ident: 10.1016/j.asoc.2023.110360_b25 article-title: A steering-matrix-based multiobjective evolutionary algorithm for high-dimensional feature selection publication-title: IEEE Trans. Cybern. – year: 2014 ident: 10.1016/j.asoc.2023.110360_b54 – volume: 24 start-page: 170 year: 2020 ident: 10.1016/j.asoc.2023.110360_b10 article-title: Multiple reference points-based decomposition for multiobjective feature selection in classification: static and dynamic mechanisms publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2019.2913831 – volume: 135 year: 2023 ident: 10.1016/j.asoc.2023.110360_b49 article-title: A high-dimensional feature selection method based on modified Gray Wolf Optimization publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2023.110031 – volume: 12 start-page: 43 year: 2017 ident: 10.1016/j.asoc.2023.110360_b33 article-title: Pattern recommendation in task-oriented applications: a multi-objective perspective [Application notes] publication-title: IEEE Comput. Intell. Mag. doi: 10.1109/MCI.2017.2708578 – volume: 87 year: 2020 ident: 10.1016/j.asoc.2023.110360_b6 article-title: Strong approximate Markov blanket and its application on filter-based feature selection publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2019.105957 – volume: 47 start-page: 2824 year: 2017 ident: 10.1016/j.asoc.2023.110360_b40 article-title: Decomposition-based-sorting and angle-based-selection for evolutionary multiobjective and many-objective optimization publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2016.2586191 – start-page: 1 year: 2021 ident: 10.1016/j.asoc.2023.110360_b27 article-title: Clustering-guided particle swarm feature selection algorithm for high-dimensional imbalanced data with missing values publication-title: IEEE Trans. Evolut. Comput. – volume: 32 start-page: 29 year: 2008 ident: 10.1016/j.asoc.2023.110360_b29 article-title: Improved binary PSO for feature selection using gene expression data publication-title: Comput. Biol. Chem. doi: 10.1016/j.compbiolchem.2007.09.005 – start-page: 769 year: 2007 ident: 10.1016/j.asoc.2023.110360_b51 article-title: Fitness functions in genetic programming for classification with unbalanced data – volume: 116 year: 2022 ident: 10.1016/j.asoc.2023.110360_b23 article-title: A hypervolume distribution entropy guided computation resource allocation mechanism for the multiobjective evolutionary algorithm based on decomposition publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2021.108297 – volume: 60 start-page: 190 year: 2017 ident: 10.1016/j.asoc.2023.110360_b30 article-title: A credit ranking model for a parafinancial company based on the ELECTRE-III method and a multiobjective evolutionary algorithm publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2017.06.021 – volume: 213 year: 2023 ident: 10.1016/j.asoc.2023.110360_b22 article-title: A new adaptive decomsposition-based evolutionary algorithm for multi- and many-objective optimization publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2022.119080 – volume: 11 start-page: 712 year: 2007 ident: 10.1016/j.asoc.2023.110360_b37 article-title: MOEA/D: a multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2007.892759 – volume: 11 start-page: 10 issue: 2009 year: 2009 ident: 10.1016/j.asoc.2023.110360_b53 article-title: The WEKA data mining software: An update publication-title: CM SIGKDD Explorations Newslett doi: 10.1145/1656274.1656278 – volume: 507 start-page: 67 year: 2020 ident: 10.1016/j.asoc.2023.110360_b45 article-title: Binary differential evolution with self-learning for multi-objective feature selection publication-title: Inform. Sci. doi: 10.1016/j.ins.2019.08.040 – volume: 134 year: 2023 ident: 10.1016/j.asoc.2023.110360_b48 article-title: A feature selection approach based on NSGA-II with relieff publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2023.109987 – volume: 28 start-page: 227 year: 2020 ident: 10.1016/j.asoc.2023.110360_b38 article-title: What weights work for you? adapting weights for any pareto front shape in decomposition-based evolutionary multiobjective optimisation publication-title: Evolut. Comput. doi: 10.1162/evco_a_00269 – ident: 10.1016/j.asoc.2023.110360_b52 – volume: 394 start-page: 70 year: 2020 ident: 10.1016/j.asoc.2023.110360_b32 article-title: A subregion division based multi-objective evolutionary algorithm for SVM training set selection publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.02.028 – volume: 14 start-page: 64 year: 2017 ident: 10.1016/j.asoc.2023.110360_b18 article-title: Multi-objective particle swarm optimization approach for cost-based feature selection in classification publication-title: IEEE/ACM Trans. Comput. Biol. Bioinform. doi: 10.1109/TCBB.2015.2476796 – volume: 91 year: 2020 ident: 10.1016/j.asoc.2023.110360_b3 article-title: Feature selection based on regularization of sparsity based regression models by hesitant fuzzy correlation publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2020.106255 – volume: 547 start-page: 841 year: 2021 ident: 10.1016/j.asoc.2023.110360_b26 article-title: A problem-specific non-dominated sorting genetic algorithm for supervised feature selection publication-title: Inform. Sci. doi: 10.1016/j.ins.2020.08.083 – volume: 9 start-page: 11 year: 1963 ident: 10.1016/j.asoc.2023.110360_b12 article-title: On the effectiveness of receptors in recognition systems publication-title: IEEE Trans. Inform. Theory doi: 10.1109/TIT.1963.1057810 – volume: 1 start-page: 131 year: 1997 ident: 10.1016/j.asoc.2023.110360_b1 article-title: Feature selection for classification publication-title: Intell. Data Anal. doi: 10.3233/IDA-1997-1302 – volume: 6 start-page: 1 year: 2021 ident: 10.1016/j.asoc.2023.110360_b19 article-title: Multi-objective feature selection with missing data in classification publication-title: IEEE Trans. Emerg. Top. Comput. Intell. – volume: 565 start-page: 278 year: 2021 ident: 10.1016/j.asoc.2023.110360_b44 article-title: A novel wrapper-based feature subset selection method using modified binary differential evolution algorithm publication-title: Inform. Sci. doi: 10.1016/j.ins.2021.02.061 – volume: 52 start-page: 1 year: 2020 ident: 10.1016/j.asoc.2023.110360_b4 article-title: Modified BBO-Based multivariate time-series prediction system with feature subset selection and model parameter optimization publication-title: IEEE Trans. Cybern. – volume: 74 start-page: 2914 issue: 2011 year: 2011 ident: 10.1016/j.asoc.2023.110360_b14 article-title: A new local search based hybrid genetic algorithm for feature selection publication-title: Neurocomputing doi: 10.1016/j.neucom.2011.03.034 – volume: 43 start-page: 1656 year: 2013 ident: 10.1016/j.asoc.2023.110360_b15 article-title: Particle swarm optimization for feature selection in classification: a multi-objective approach publication-title: IEEE Trans. Cybern. doi: 10.1109/TSMCB.2012.2227469 – volume: 52 start-page: 8326 year: 2022 ident: 10.1016/j.asoc.2023.110360_b31 article-title: Multiobjective optimization-aided decision-making system for large-scale manufacturing planning publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2021.3049712 – volume: 24 start-page: 720 year: 2020 ident: 10.1016/j.asoc.2023.110360_b39 article-title: A framework to handle multimodal multiobjective optimization in decomposition-based evolutionary algorithms publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2019.2949841 – volume: 21 start-page: 660 year: 1991 ident: 10.1016/j.asoc.2023.110360_b13 article-title: A survey of decision tree classifier methodology publication-title: IEEE Trans. Syst. Man Cybern. doi: 10.1109/21.97458 – volume: 67 start-page: 94 year: 2018 ident: 10.1016/j.asoc.2023.110360_b8 article-title: Dealing with high-dimensional class-imbalanced datasets: embedded feature selection for SVM classification publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2018.02.051 – volume: 23 start-page: 556 year: 2019 ident: 10.1016/j.asoc.2023.110360_b35 article-title: A generator for multiobjective test problems with difficult-to-approximate Pareto front boundaries publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2018.2872453 – year: 2023 ident: 10.1016/j.asoc.2023.110360_b50 article-title: An evolutionary multitasking algorithm with multiple filtering for high-dimensional feature selection publication-title: IEEE Trans. Evolut. Comput. doi: 10.1109/TEVC.2023.3254155 – volume: 20 start-page: 3927 year: 2016 ident: 10.1016/j.asoc.2023.110360_b17 article-title: New mechanism for archive maintenance in PSO-based multi-objective feature selection publication-title: Soft Comput doi: 10.1007/s00500-016-2128-8 – volume: 101 year: 2021 ident: 10.1016/j.asoc.2023.110360_b24 article-title: Multi-objective optimization using self-organizing decomposition and its applications to crashworthiness design publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2020.107002 – volume: 112 year: 2021 ident: 10.1016/j.asoc.2023.110360_b43 article-title: Feature selection using bare-bones particle swarm optimization with mutual information publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2020.107804 – ident: 10.1016/j.asoc.2023.110360_b28 doi: 10.1109/CIDM.2009.4938668 – start-page: 1 year: 2021 ident: 10.1016/j.asoc.2023.110360_b42 article-title: A fast hybrid feature selection based on correlation-guided clustering and particle swarm optimization for high-dimensional data publication-title: IEEE Trans. Cybern. – volume: 48 start-page: 1733 year: 2018 ident: 10.1016/j.asoc.2023.110360_b9 article-title: A new representation in PSO for discretization-based feature selection publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2017.2714145 – volume: C-20 start-page: 1100 year: 1971 ident: 10.1016/j.asoc.2023.110360_b11 article-title: A direct method of nonparametric measurement selection publication-title: IEEE Trans. Comput. doi: 10.1109/T-C.1971.223410 – volume: 48 start-page: 329 year: 2018 ident: 10.1016/j.asoc.2023.110360_b7 article-title: Sparse graph embedding unsupervised feature selection publication-title: IEEE Trans. Syst. Man Cybern. Syst. doi: 10.1109/TSMC.2016.2605132 – year: 2022 ident: 10.1016/j.asoc.2023.110360_b36 article-title: An immune-inspired resources allocation strategy for many-objective optimization publication-title: IEEE Trans. Syst. Man Cybern. Syst. – volume: 25 start-page: 1 year: 2013 ident: 10.1016/j.asoc.2023.110360_b41 article-title: A fast clustering-based feature subset selection algorithm for high-dimensional data publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2011.181 – volume: 98 year: 2021 ident: 10.1016/j.asoc.2023.110360_b2 article-title: Balancing the user-driven feature selection and their incidence in the clustering structure formation publication-title: Appl. Soft Comput. – volume: 51 start-page: 3115 year: 2021 ident: 10.1016/j.asoc.2023.110360_b47 article-title: Solving large-scale multiobjective optimization problems with sparse optimal solutions via unsupervised neural networks publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2020.2979930 – volume: 23 start-page: 473 year: 2019 ident: 10.1016/j.asoc.2023.110360_b46 article-title: Variable-length particle swarm optimization for feature selection on high-dimensional classification publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2018.2869405 – volume: 892 year: 2017 ident: 10.1016/j.asoc.2023.110360_b21 article-title: A review on missing value estimation using imputation algorithm publication-title: J. Phys.: Conf. Ser. – volume: 18 start-page: 577 year: 2014 ident: 10.1016/j.asoc.2023.110360_b20 article-title: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach part I: solving problems with box constraints publication-title: IEEE Trans. Evolut. Comput. doi: 10.1109/TEVC.2013.2281535 – volume: 50 start-page: 703 year: 2020 ident: 10.1016/j.asoc.2023.110360_b34 article-title: A network reduction-based multiobjective evolutionary algorithm for community detection in large-scale complex networks publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2018.2871673 |
| SSID | ssj0016928 |
| Score | 2.482537 |
| Snippet | Feature selection (FS) is an important technique in data preprocessing that aims to reduce the number of features for training while maintaining a high... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 110360 |
| SubjectTerms | Evolutionary algorithm Feature selection High-dimensional classification Multiobjective optimization Resource allocation |
| Title | Multiobjective optimization algorithm with dynamic operator selection for feature selection in high-dimensional classification |
| URI | https://dx.doi.org/10.1016/j.asoc.2023.110360 |
| Volume | 143 |
| WOSCitedRecordID | wos001053604200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 issn: 1568-4946 databaseCode: AIEXJ dateStart: 20010601 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0016928 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELaWlgMXKC9RoMgHblGqPJw4PlaoqEVQgSiwtyh-pJvVNql2s6XiwG9n_Mput6gCJC7RamQ7q3yfxuPxPBB6TWQepQkXIVjHNCSM16Gu-hTC1klqzjgXJr_i63t6clKMx-zjaPTd58JczmjbFldX7OK_Qg0yAFunzv4F3MOiIIDfADo8AXZ4_hHwJqW241OryYIOdMK5S7YMqtlZN2_6ybn1v0rbjh7GKHPbHixMVxwffVgrU_VzTdq0ga5vHErdE8DW8wiEtr91wNEKY1_V1lm4C1D1JnZ92fuN0twFmUCCb6qddCvpeGk9sh8qXcJjCBdqnP_gbNqoldCM_NQsf0xcQptzXyTpEDznfGo38mqsGs4LII5zTno9bes53dD51v0w3a-Azvv6FTq1IbVdCjZqaX_WC-t14eAVpWAc3UHbCc0YaPTtg-PD8bvhAipnpi3v8EdcvpUNDdx80-9tmjU75XQH3XcHDHxgifEQjVT7CD3wzTuw0-WP0c_rPMHrPMEDT7DmCXY8wZ4neGAEBp5gx5M1adPiTZ7g6zx5gr68PTx9cxS6ZhyhgG_VhznhNNO1hIpM5pmghNRxomqeJxFNqiJnqazANI65EDStY14nghayhgMBT-IUjOSnaKvtWvUMYUmKmEpS8UjEJCsiVieM51LvJkpIEe-i2H_OUrhK9bphyqz0IYnTUkNQaghKC8EuCoY5F7ZOy62jM49S6SxNa0GWQKpb5j3_x3kv0L0V91-irX6-VHvorrjsm8X8lePeL9drqdg |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Multiobjective+optimization+algorithm+with+dynamic+operator+selection+for+feature+selection+in+high-dimensional+classification&rft.jtitle=Applied+soft+computing&rft.au=Wei%2C+Wenhong&rft.au=Xuan%2C+Manlin&rft.au=Li%2C+Lingjie&rft.au=Lin%2C+Qiuzhen&rft.date=2023-08-01&rft.pub=Elsevier+B.V&rft.issn=1568-4946&rft.volume=143&rft_id=info:doi/10.1016%2Fj.asoc.2023.110360&rft.externalDocID=S1568494623003782 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1568-4946&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1568-4946&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1568-4946&client=summon |