{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,31]],"date-time":"2024-08-31T12:48:01Z","timestamp":1725108481381},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T00:00:00Z","timestamp":1687996800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T00:00:00Z","timestamp":1687996800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076089","61976120"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s13042-023-01897-4","type":"journal-article","created":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T14:02:18Z","timestamp":1688047338000},"page":"4339-4360","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Feature selection using symmetric uncertainty and hybrid optimization for high-dimensional data"],"prefix":"10.1007","volume":"14","author":[{"given":"Lin","family":"Sun","sequence":"first","affiliation":[]},{"given":"Shujing","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Weiping","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Xinyue","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Peiyi","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Kunyu","family":"Li","sequence":"additional","affiliation":[]},{"given":"Leqi","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,29]]},"reference":[{"issue":"1","key":"1897_CR1","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/TETCI.2022.3171784","volume":"7","author":"WH Xu","year":"2023","unstructured":"Xu WH, Yuan KH, Li WT, Ding WP (2023) An emerging fuzzy feature selection method using composite entropy-based uncertainty measure and data distribution. IEEE Transact Emerg Top Computat Intellig 7(1):76\u201388","journal-title":"IEEE Transact Emerg Top Computat Intellig"},{"key":"1897_CR2","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2022.3222941","author":"L Sun","year":"2022","unstructured":"Sun L, Wang TX, Ding WP, Xu JC (2022) Partial multilabel learning using fuzzy neighbourhood-based ball clustering and kernel extreme learning machine. IEEE Trans Fuzzy Syst. https:\/\/doi.org\/10.1109\/TFUZZ.2022.3222941","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"4","key":"1897_CR3","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1109\/TEVC.2015.2504420","volume":"20","author":"B Xue","year":"2016","unstructured":"Xue B, Zhang MJ, Browne WN, Yao X (2016) A survey on evolutionary computation approaches to feature selection. IEEE Trans Evol Comput 20(4):606\u2013626","journal-title":"IEEE Trans Evol Comput"},{"key":"1897_CR4","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3184120","author":"WT Li","year":"2022","unstructured":"Li WT, Zhou HX, Xu WH, Wang XZ, Pedrycz W (2022) Interval dominance-based feature selection for interval-valued ordered data. IEEE Transact Neural Net Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2022.3184120","journal-title":"IEEE Transact Neural Net Learn Syst"},{"issue":"7","key":"1897_CR5","doi-asserted-by":"publisher","first-page":"7172","DOI":"10.1109\/TCYB.2020.3042243","volume":"52","author":"K Chen","year":"2022","unstructured":"Chen K, Xue B, Zhang MJ, Zhou FY (2022) An evolutionary multitasking-based feature selection method for high-dimensional classification. IEEE Transact Cybernet 52(7):7172\u20137186","journal-title":"IEEE Transact Cybernet"},{"key":"1897_CR6","doi-asserted-by":"publisher","first-page":"724","DOI":"10.1016\/j.ins.2022.08.118","volume":"612","author":"L Sun","year":"2022","unstructured":"Sun L, Li MM, Ding WP, Zhang E, Mu XX, Xu JC (2022) AFNFS: Adaptive fuzzy neighborhood-based feature selection with adaptive synthetic over-sampling for imbalanced data. Inf Sci 612:724\u2013744","journal-title":"Inf Sci"},{"issue":"1","key":"1897_CR7","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1109\/TFUZZ.2020.2989098","volume":"29","author":"L Sun","year":"2021","unstructured":"Sun L, Wang LY, Ding WP, Qian YH, Xu JC (2021) Feature selection using fuzzy neighborhood entropy-based uncertainty measures for fuzzy neighborhood multigranulation rough sets. IEEE Trans Fuzzy Syst 29(1):19\u201333","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"6","key":"1897_CR8","first-page":"3016","volume":"34","author":"XF Zhu","year":"2022","unstructured":"Zhu XF, Zhang SC, Zhu YH, Zhu PF, Gao Y (2022) Unsupervised spectral feature selection with dynamic hyper-graph learning. IEEE Trans Knowl Data Eng 34(6):3016\u20133028","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"25","key":"1897_CR9","doi-asserted-by":"publisher","first-page":"110111","DOI":"10.1016\/j.knosys.2022.110111","volume":"260","author":"YB Zhu","year":"2023","unstructured":"Zhu YB, Li WS, Li T (2023) A hybrid artificial immune optimization for high-dimensional feature selection. Knowl-Based Syst 260(25):110111","journal-title":"Knowl-Based Syst"},{"key":"1897_CR10","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2022.3216110","author":"WH Xu","year":"2022","unstructured":"Xu WH, Guo DD, Qian YH, Ding WP (2022) Two-way concept-cognitive learning method: a fuzzy-based progressive learning. IEEE Trans Fuzzy Syst. https:\/\/doi.org\/10.1109\/TFUZZ.2022.3216110","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"1897_CR11","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3235800","author":"WH Xu","year":"2023","unstructured":"Xu WH, Guo DD, Mi JS, Qian YH, Zheng KY, Ding WP (2023) Two-way concept-cognitive learning via concept movement viewpoint. IEEE Transact Neu Net Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2023.3235800","journal-title":"IEEE Transact Neu Net Learn Syst"},{"key":"1897_CR12","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2022.3215129","author":"Y Kang","year":"2022","unstructured":"Kang Y, Wang HN, Pu B, Tao L, Chen JG, Yu PS (2022) A hybrid two-stage teaching-learning-based optimization algorithm for feature selection in bioinformatics. IEEE\/ACM Trans Comput Biol Bioinf. https:\/\/doi.org\/10.1109\/TCBB.2022.3215129","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"1897_CR13","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1016\/j.ins.2022.02.004","volume":"593","author":"L Sun","year":"2022","unstructured":"Sun L, Zhang JX, Ding WP, Xu JC (2022) Feature reduction for imbalanced data classification using similarity-based feature clustering with adaptive weighted k-nearest neighbors. Inf Sci 593:591\u2013613","journal-title":"Inf Sci"},{"key":"1897_CR14","doi-asserted-by":"publisher","first-page":"107560","DOI":"10.1016\/j.knosys.2021.107560","volume":"234","author":"Z Halim","year":"2021","unstructured":"Halim Z (2021) An ensemble filter-based heuristic approach for cancerous gene expression classification. Knowl-Based Syst 234:107560","journal-title":"Knowl-Based Syst"},{"key":"1897_CR15","doi-asserted-by":"publisher","first-page":"13845","DOI":"10.1109\/ACCESS.2021.3049815","volume":"9","author":"L Zhang","year":"2021","unstructured":"Zhang L, Chen XB (2021) Feature selection methods based on symmetric uncertainty coefficients and independent classification information. IEEE access 9:13845\u201313856","journal-title":"IEEE access"},{"issue":"1","key":"1897_CR16","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/s13042-019-00932-7","volume":"11","author":"S Bakhshandeh","year":"2020","unstructured":"Bakhshandeh S, Azmi R, Teshnehlab M (2020) Symmetric uncertainty class-feature association map for feature selection in microarray dataset. Int J Mach Learn Cybern 11(1):15\u201332","journal-title":"Int J Mach Learn Cybern"},{"key":"1897_CR17","doi-asserted-by":"publisher","first-page":"101286","DOI":"10.1016\/j.swevo.2023.101286","volume":"78","author":"ZY Chai","year":"2023","unstructured":"Chai ZY, Li WW, Li YL (2023) Symmetric uncertainty based decomposition multi-objective immune algorithm for feature selection. Swarm Evol Comput 78:101286","journal-title":"Swarm Evol Comput"},{"key":"1897_CR18","doi-asserted-by":"publisher","first-page":"2754","DOI":"10.1109\/ACCESS.2023.3234597","volume":"11","author":"S Lee","year":"2023","unstructured":"Lee S, Lee GS (2023) Automatic features extraction integrated with exact Gaussian process for respiratory rate and uncertainty estimations. IEEE access 11:2754\u20132766","journal-title":"IEEE access"},{"issue":"1","key":"1897_CR19","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1111\/coin.12192","volume":"35","author":"XY Zhu","year":"2019","unstructured":"Zhu XY, Wang Y, Li YB, Tan YH, Wang GT, Song QB (2019) A new unsupervised feature selection algorithm using similarity-based feature clustering. Comput Intell 35(1):2\u201322","journal-title":"Comput Intell"},{"key":"1897_CR20","doi-asserted-by":"publisher","first-page":"108154","DOI":"10.1016\/j.patcog.2021.108154","volume":"120","author":"WC Zhong","year":"2021","unstructured":"Zhong WC, Chen XJ, Wu QY, Yang M, Huang JZ (2021) Selection of diverse features with a diverse regularization. Pattern Recogn 120:108154","journal-title":"Pattern Recogn"},{"key":"1897_CR21","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.patrec.2019.12.022","volume":"131","author":"XY Yan","year":"2020","unstructured":"Yan XY, Nazmi S, Erol BA, Homaifar A, Gebru B, Tunstel E (2020) An efficient unsupervised feature selection procedure through feature clustering. Pattern Recogn Lett 131:277\u2013284","journal-title":"Pattern Recogn Lett"},{"issue":"1","key":"1897_CR22","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/s10044-016-0565-8","volume":"21","author":"Z Dehghan","year":"2018","unstructured":"Dehghan Z, Mansoori EG (2018) A new feature subset selection using bottom-up clustering. Pattern Anal Appl 21(1):57\u201366","journal-title":"Pattern Anal Appl"},{"key":"1897_CR23","doi-asserted-by":"publisher","unstructured":"Liu Q, Zhang JX, Xiao JK, Zhu HM, Zhao QP, A supervised feature selection algorithm through minimum spanning tree clustering. In: IEEE 26th international conference on tools with artificial intelligence, (2014) doi: https:\/\/doi.org\/10.1109\/ICTAI.2014.47.","DOI":"10.1109\/ICTAI.2014.47"},{"key":"1897_CR24","doi-asserted-by":"publisher","unstructured":"Kennedy J, Eberhart R, Particle swarm optimization. In: Proceedings of ICNN\u201995-international conference on neural networks (1995) doi: https:\/\/doi.org\/10.1109\/ICNN.1995.488968.","DOI":"10.1109\/ICNN.1995.488968"},{"key":"1897_CR25","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2022.3175226","author":"XF Song","year":"2022","unstructured":"Song XF, Zhang Y, Gong DW, Liu H, Zhang WQ (2022) Surrogate sample-assisted particle swarm optimization for feature selection on high-dimensional data. IEEE Trans Evol Comput. https:\/\/doi.org\/10.1109\/TEVC.2022.3175226","journal-title":"IEEE Trans Evol Comput"},{"key":"1897_CR26","doi-asserted-by":"publisher","first-page":"107394","DOI":"10.1016\/j.asoc.2021.107394","volume":"107","author":"P Dhal","year":"2021","unstructured":"Dhal P, Azad C (2021) A multi-objective feature selection method using Newton\u2019s law based PSO with GWO. Appl Soft Comput 107:107394","journal-title":"Appl Soft Comput"},{"key":"1897_CR27","doi-asserted-by":"publisher","first-page":"39496","DOI":"10.1109\/ACCESS.2019.2906757","volume":"7","author":"Q Al-Tashi","year":"2019","unstructured":"Al-Tashi Q, Kadir SJA, Rais HM, Mirjalili S, Alhussian H (2019) Binary optimization using hybrid grey wolf optimization for feature selection. IEEE access 7:39496\u201339508","journal-title":"IEEE access"},{"issue":"8","key":"1897_CR28","doi-asserted-by":"publisher","first-page":"10365","DOI":"10.1007\/s13369-021-06456-z","volume":"47","author":"SR Bansal","year":"2022","unstructured":"Bansal SR, Wadhawan S, Goel R (2022) mRMR-PSO: A hybrid feature selection technique with a multiobjective approach for sign language recognition. Arab J Sci Eng 47(8):10365\u201310380","journal-title":"Arab J Sci Eng"},{"issue":"13","key":"1897_CR29","doi-asserted-by":"publisher","first-page":"18155","DOI":"10.1007\/s11042-022-12425-x","volume":"81","author":"MG El-Shafiey","year":"2022","unstructured":"El-Shafiey MG, Hagag A, El-Dahshan ESA, Ismail MA (2022) A hybrid GA and PSO optimized approach for heart-disease prediction based on random forest. Multimedia Tools Applicat 81(13):18155\u201318179","journal-title":"Multimedia Tools Applicat"},{"key":"1897_CR30","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.inffus.2023.02.016","volume":"95","author":"L Sun","year":"2023","unstructured":"Sun L, Si SS, Ding WP, Wang XY, Xu JC (2023) TFSFB, Two-stage feature selection via fusing fuzzy multi-neighborhood rough set with binary whale optimization for imbalanced data. Informat Fus 95:91\u2013108","journal-title":"Informat Fus"},{"key":"1897_CR31","doi-asserted-by":"publisher","first-page":"6773","DOI":"10.1002\/int.22861","volume":"37","author":"L Sun","year":"2022","unstructured":"Sun L, Wang TX, Ding WP, Xu JC, Tan AH (2022) Two-stage-neighborhood-based multilabel classification for incomplete data with missing labels. Int J Intell Syst 37:6773\u20136810","journal-title":"Int J Intell Syst"},{"issue":"5","key":"1897_CR32","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1109\/TEVC.2020.2968743","volume":"24","author":"XF Song","year":"2020","unstructured":"Song XF, Zhang Y, Guo YN, Sun XY, Wang YL (2020) Variable-size cooperative coevolutionary particle swarm optimization for feature selection on high-dimensional data. IEEE Trans Evol Comput 24(5):882\u2013895","journal-title":"IEEE Trans Evol Comput"},{"key":"1897_CR33","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.neucom.2022.04.083","volume":"494","author":"T Dokeroglu","year":"2022","unstructured":"Dokeroglu T, Deniz A, Kiziloz HE (2022) A comprehensive survey on recent metaheuristics for feature selection. Neurocomputing 494:269\u2013296","journal-title":"Neurocomputing"},{"key":"1897_CR34","doi-asserted-by":"publisher","first-page":"109849","DOI":"10.1016\/j.knosys.2022.109849","volume":"256","author":"L Sun","year":"2022","unstructured":"Sun L, Wang XY, Ding WP, Xu JC (2022) TSFNFR: Two-stage fuzzy neighborhood-based feature reduction with binary whale optimization algorithm for imbalanced data classification. Knowl-Based Syst 256:109849","journal-title":"Knowl-Based Syst"},{"issue":"3","key":"1897_CR35","first-page":"49","volume":"20","author":"P Ashokkumar","year":"2021","unstructured":"Ashokkumar P, Shankar GS, Srivastava G, Maddikunta PKR, Gadekallu TR (2021) A two-stage text feature selection algorithm for improving text classification. ACM Transact Asian Low-Res Lang Informat Process 20(3):49","journal-title":"ACM Transact Asian Low-Res Lang Informat Process"},{"key":"1897_CR36","doi-asserted-by":"publisher","first-page":"107933","DOI":"10.1016\/j.patcog.2021.107933","volume":"116","author":"WP Ma","year":"2021","unstructured":"Ma WP, Zhou XB, Zhu H, Li LW, Jiao LC (2021) A two-stage hybrid ant colony optimization for high-dimensional feature selection. Pattern Recogn 116:107933","journal-title":"Pattern Recogn"},{"issue":"5","key":"1897_CR37","doi-asserted-by":"publisher","first-page":"1888","DOI":"10.1109\/JBHI.2018.2872811","volume":"23","author":"ZK Huang","year":"2019","unstructured":"Huang ZK, Yang CH, Zhou XJ, Huang TW (2019) A hybrid feature selection method based on binary state transition algorithm and ReliefF. IEEE J Biomed Health Inform 23(5):1888\u20131898","journal-title":"IEEE J Biomed Health Inform"},{"issue":"9","key":"1897_CR38","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.3390\/e23091200","volume":"23","author":"Y Shen","year":"2021","unstructured":"Shen Y, Cai WZ, Kang HW, Sun XP, Chen QY, Zhang HG (2021) A particle swarm algorithm based on a multi-stage search strategy. Entropy 23(9):1200","journal-title":"Entropy"},{"key":"1897_CR39","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2022.3215494","author":"WH Xu","year":"2022","unstructured":"Xu WH, Pan YZ, Chen XW, Ding WP, Qian YH (2022) A novel dynamic fusion approach using information entropy for interval-valued ordered datasets. IEEE Transact Big Data. https:\/\/doi.org\/10.1109\/TBDATA.2022.3215494","journal-title":"IEEE Transact Big Data"},{"issue":"5","key":"1897_CR40","doi-asserted-by":"publisher","first-page":"1197","DOI":"10.1109\/TFUZZ.2021.3053844","volume":"30","author":"L Sun","year":"2022","unstructured":"Sun L, Yin TY, Ding WP, Qian YH, Xu JC (2022) Feature selection with missing labels using multilabel fuzzy neighborhood rough sets and maximum relevance minimum redundancy. IEEE Trans Fuzzy Syst 30(5):1197\u20131211","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"1897_CR41","doi-asserted-by":"publisher","first-page":"107804","DOI":"10.1016\/j.patcog.2020.107804","volume":"112","author":"XF Song","year":"2021","unstructured":"Song XF, Zhang Y, Gong DW, Sun XY (2021) Feature selection using bare-bones particle swarm optimization with mutual information. Pattern Recogn 112:107804","journal-title":"Pattern Recogn"},{"key":"1897_CR42","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.fss.2021.07.015","volume":"438","author":"M Rahmanian","year":"2022","unstructured":"Rahmanian M, Mansoori E (2022) Unsupervised fuzzy multivariate symmetric uncertainty feature selection based on constructing virtual cluster representative. Fuzzy Sets Syst 438:148\u2013163","journal-title":"Fuzzy Sets Syst"},{"issue":"9","key":"1897_CR43","doi-asserted-by":"publisher","first-page":"9573","DOI":"10.1109\/TCYB.2021.3061152","volume":"52","author":"XF Song","year":"2022","unstructured":"Song XF, Zhang Y, Gong DW, Gao XZ (2022) A fast hybrid feature selection based on correlation-guided clustering and particle swarm optimization for high-dimensional data. IEEE Transact Cybernet 52(9):9573\u20139586","journal-title":"IEEE Transact Cybernet"},{"issue":"4","key":"1897_CR44","doi-asserted-by":"publisher","first-page":"3025","DOI":"10.1007\/s00366-021-01438-z","volume":"38","author":"I Naruei","year":"2022","unstructured":"Naruei I, Keynia F (2022) Wild horse optimizer: a new meta-heuristic algorithm for solving engineering optimization problems. Eng Comput 38(4):3025\u20133056","journal-title":"Eng Comput"},{"key":"1897_CR45","doi-asserted-by":"publisher","first-page":"4182148","DOI":"10.1155\/2019\/4182148","volume":"2019","author":"L Sun","year":"2019","unstructured":"Sun L, Chen SS, Xu JC, Tian Y (2019) Improved monarch butterfly optimization algorithm based on opposition-based learning and random local perturbation. Complexity 2019:4182148","journal-title":"Complexity"},{"issue":"10","key":"1897_CR46","doi-asserted-by":"publisher","first-page":"2200097","DOI":"10.1002\/aisy.202200097","volume":"4","author":"YC Li","year":"2022","unstructured":"Li YC, Yuan QY, Han MX, Cui R (2022) Hybrid multi-strategy improved wild horse optimizer. Adv Intell Syst 4(10):2200097","journal-title":"Adv Intell Syst"},{"key":"1897_CR47","doi-asserted-by":"publisher","first-page":"106258","DOI":"10.1109\/ACCESS.2022.3211263","volume":"10","author":"AA Ewees","year":"2022","unstructured":"Ewees AA, Ismail FH, Ghoniem RM (2022) Wild horse optimizer-based spiral updating for feature selection. IEEE Access 10:106258\u2013106274","journal-title":"IEEE Access"},{"key":"1897_CR48","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1016\/j.ins.2021.08.032","volume":"578","author":"L Sun","year":"2021","unstructured":"Sun L, Wang TX, Ding WP, Xu JC, Lin YJ (2021) Feature selection using Fisher score and multilabel neighborhood rough sets for multilabel classification. Inf Sci 578:887\u2013912","journal-title":"Inf Sci"},{"key":"1897_CR49","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1016\/j.ins.2020.05.102","volume":"537","author":"L Sun","year":"2020","unstructured":"Sun L, Yin TY, Ding WP, Qian YH, Xu JC (2020) Multilabel feature selection using ML-ReliefF and neighborhood mutual information for multilabel neighborhood decision systems. Inf Sci 537:401\u2013424","journal-title":"Inf Sci"},{"issue":"6","key":"1897_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3136625","volume":"50","author":"JD Li","year":"2018","unstructured":"Li JD, Cheng KW, Wang SH, Morstatter F, Trevino RP, Tang J, Liu H (2018) Feature selection: a data perspective. ACM Comput Surv 50(6):1\u201345","journal-title":"ACM Comput Surv"},{"key":"1897_CR51","doi-asserted-by":"publisher","first-page":"105373","DOI":"10.1016\/j.knosys.2019.105373","volume":"192","author":"L Sun","year":"2020","unstructured":"Sun L, Wang LY, Ding WP, Qian YH, Xu JC (2020) Neighborhood multi-granulation rough sets-based attribute reduction using Lebesgue and entropy measures in incomplete neighborhood decision systems. Knowl-Based Syst 192:105373","journal-title":"Knowl-Based Syst"},{"key":"1897_CR52","first-page":"1205","volume":"5","author":"L Yu","year":"2004","unstructured":"Yu L, Liu H (2004) Efficient feature selection via analysis of relevance and redundancy. J Mach Learn Res 5:1205\u20131224","journal-title":"J Mach Learn Res"},{"key":"1897_CR53","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.procs.2013.05.011","volume":"17","author":"X Zhao","year":"2013","unstructured":"Zhao X, Deng W, Shi Y (2013) Feature selection with attributes clustering by maximal information coefficient. Procedia Comp Sci 17:70\u201379","journal-title":"Procedia Comp Sci"},{"issue":"8","key":"1897_CR54","doi-asserted-by":"publisher","first-page":"9148","DOI":"10.1007\/s10489-021-02861-x","volume":"52","author":"WH Xu","year":"2022","unstructured":"Xu WH, Yuan KH, Li WT (2022) Dynamic updating approximations of local generalized multigranulation neighborhood rough set. Appl Intell 52(8):9148\u20139173","journal-title":"Appl Intell"},{"issue":"6","key":"1897_CR55","first-page":"1155","volume":"15","author":"QH Mao","year":"2021","unstructured":"Mao QH, Zhang Q (2021) Improved sparrow algorithm combining cauchy mutation and opposition-based learning. J Front Comp Sci Technol 15(6):1155\u20131164","journal-title":"J Front Comp Sci Technol"},{"key":"1897_CR56","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-021-03621-y","author":"K Balakrishnan","year":"2022","unstructured":"Balakrishnan K, Dhanalakshmi R, Khaire UM (2022) A novel control factor and Brownian motion-based improved Harris Hawks Optimization for feature selection. J Ambient Intell Humaniz Comput. https:\/\/doi.org\/10.1007\/s12652-021-03621-y","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"1897_CR57","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1016\/j.ins.2017.08.047","volume":"418\u2013419","author":"Y Zhang","year":"2017","unstructured":"Zhang Y, Song XF, Gong DW (2017) A return-cost-based binary firefly algorithm for feature selection. Inf Sci 418\u2013419:561\u2013574","journal-title":"Inf Sci"},{"key":"1897_CR58","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.eswa.2019.03.039","volume":"128","author":"K Chen","year":"2019","unstructured":"Chen K, Zhou FY, Yuan XF (2019) Hybrid particle swarm optimization with spiral-shaped mechanism for feature selection. Expert Syst Appl 128:140\u2013156","journal-title":"Expert Syst Appl"},{"issue":"5","key":"1897_CR59","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1145\/3340848","volume":"13","author":"Y Xue","year":"2019","unstructured":"Xue Y, Xue B, Zhang MJ (2019) Self-adaptive particle swarm optimization for large-scale feature selection in classification. ACM Trans Knowl Discov Data 13(5):50","journal-title":"ACM Trans Knowl Discov Data"},{"issue":"10","key":"1897_CR60","doi-asserted-by":"publisher","first-page":"13367","DOI":"10.1016\/j.eswa.2011.04.165","volume":"38","author":"LY Chuang","year":"2011","unstructured":"Chuang LY, Yang CS, Wu KC, Yang CH (2011) Gene selection and classification using Taguchi chaotic binary particle swarm optimization. Expert Syst Appl 38(10):13367\u201313377","journal-title":"Expert Syst Appl"},{"issue":"11","key":"1897_CR61","doi-asserted-by":"publisher","first-page":"9191","DOI":"10.1007\/s13369-019-04064-6","volume":"44","author":"G Ansari","year":"2019","unstructured":"Ansari G, Ahmad T, Doja MN (2019) Hybrid filter-wrapper feature selection method for sentiment classification. Arab J Sci Eng 44(11):9191\u20139208","journal-title":"Arab J Sci Eng"},{"key":"1897_CR62","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.neucom.2012.09.049","volume":"148","author":"Y Zhang","year":"2015","unstructured":"Zhang Y, Gong DW, Hu Y, Zhang WQ (2015) Feature selection algorithm based on bare bones particle swarm optimization. Neurocomputing 148:150\u2013157","journal-title":"Neurocomputing"},{"key":"1897_CR63","doi-asserted-by":"publisher","first-page":"80588","DOI":"10.1109\/ACCESS.2019.2919956","volume":"7","author":"Q Wu","year":"2019","unstructured":"Wu Q, Ma ZP, Fan J, Xu G, Shen YF (2019) A feature selection method based on hybrid improved binary quantum particle swarm optimization. IEEE access 7:80588\u201380601","journal-title":"IEEE access"},{"key":"1897_CR64","doi-asserted-by":"publisher","first-page":"108800","DOI":"10.1016\/j.asoc.2022.108800","volume":"121","author":"YY Yang","year":"2022","unstructured":"Yang YY, Chen DG, Zhang X, Ji ZY, Zhang YJ (2022) Incremental feature selection by sample selection and feature-based accelerator. Appl Soft Comput 121:108800","journal-title":"Appl Soft Comput"},{"issue":"1","key":"1897_CR65","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1080\/21642583.2019.1708830","volume":"8","author":"JK Xue","year":"2021","unstructured":"Xue JK, Shen B (2021) A novel swarm intelligence optimization approach: sparrow search algorithm. Syst Sci Cont Eng 8(1):22\u201334","journal-title":"Syst Sci Cont Eng"},{"issue":"1","key":"1897_CR66","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1214\/aoms\/1177731944","volume":"11","author":"M Friedman","year":"1940","unstructured":"Friedman M (1940) A comparison of alternative tests of significance for the problem of m rankings. Ann Math Stat 11(1):86\u201392","journal-title":"Ann Math Stat"},{"key":"1897_CR67","first-page":"1","volume":"7","author":"J Demsar","year":"2006","unstructured":"Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330","journal-title":"J Mach Learn Res"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01897-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-023-01897-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01897-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T06:27:15Z","timestamp":1697178435000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-023-01897-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,29]]},"references-count":67,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["1897"],"URL":"https:\/\/doi.org\/10.1007\/s13042-023-01897-4","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,29]]},"assertion":[{"value":"25 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 June 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The work described is not under consideration for publication elsewhere; all the necessary files have been uploaded by online; each author has participated sufficiently; and all the authors listed have approved the manuscript that is enclosed.The authors state that this research complies with ethical standards. This research does not involve either human participants or animals.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The authors state that this research complies with ethical standards. This research does not involve either human participants or animals.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}