{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T05:44:15Z","timestamp":1780638255234,"version":"3.54.1"},"reference-count":108,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T00:00:00Z","timestamp":1713916800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T00:00:00Z","timestamp":1713916800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1007\/s13042-024-02143-1","type":"journal-article","created":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T09:02:23Z","timestamp":1713949343000},"page":"4211-4254","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Feature selection in high-dimensional data: an enhanced RIME optimization with information entropy pruning and DBSCAN clustering"],"prefix":"10.1007","volume":"15","author":[{"given":"Huangying","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhennao","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali Asghar","family":"Heidari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiling","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,24]]},"reference":[{"issue":"22","key":"2143_CR1","doi-asserted-by":"crossref","first-page":"8520","DOI":"10.1016\/j.eswa.2015.07.007","volume":"42","author":"M Bennasar","year":"2015","unstructured":"Bennasar M, Hicks Y, Setchi R (2015) Feature selection using joint mutual information maximisation. Expert Syst Appl 42(22):8520\u20138532","journal-title":"Expert Syst Appl"},{"key":"2143_CR2","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.neucom.2015.06.083","volume":"172","author":"E Emary","year":"2016","unstructured":"Emary E, Zawbaa HM, Hassanien AE (2016) Binary grey wolf optimization approaches for feature selection. Neurocomputing 172:371\u2013381","journal-title":"Neurocomputing"},{"issue":"10","key":"2143_CR3","doi-asserted-by":"crossref","first-page":"2986","DOI":"10.1109\/TC.2016.2519914","volume":"65","author":"MA Ambusaidi","year":"2016","unstructured":"Ambusaidi MA et al (2016) Building an intrusion detection system using a filter-based feature selection algorithm. IEEE Trans Comput 65(10):2986\u20132998","journal-title":"IEEE Trans Comput"},{"issue":"5","key":"2143_CR4","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1109\/TCBB.2015.2478454","volume":"13","author":"JC Ang","year":"2015","unstructured":"Ang JC et al (2015) Supervised, unsupervised, and semi-supervised feature selection: a review on gene selection. IEEE\/ACM Trans Comput Biol Bioinf 13(5):971\u2013989","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"issue":"5","key":"2143_CR5","doi-asserted-by":"crossref","first-page":"2615","DOI":"10.1109\/TGRS.2015.2503885","volume":"54","author":"C Persello","year":"2015","unstructured":"Persello C, Bruzzone L (2015) Kernel-based domain-invariant feature selection in hyperspectral images for transfer learning. IEEE Trans Geosci Remote Sens 54(5):2615\u20132626","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"2143_CR6","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2019.103375","volume":"112","author":"B Remeseiro","year":"2019","unstructured":"Remeseiro B, Bolon-Canedo V (2019) A review of feature selection methods in medical applications. Comput Biol Med 112:103375","journal-title":"Comput Biol Med"},{"key":"2143_CR7","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.jbi.2018.07.014","volume":"85","author":"RJ Urbanowicz","year":"2018","unstructured":"Urbanowicz RJ et al (2018) Relief-based feature selection: introduction and review. J Biomed Inform 85:189\u2013203","journal-title":"J Biomed Inform"},{"issue":"4","key":"2143_CR8","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1109\/TEVC.2015.2504420","volume":"20","author":"B Xue","year":"2015","unstructured":"Xue B et al (2015) A survey on evolutionary computation approaches to feature selection. IEEE Trans Evol Comput 20(4):606\u2013626","journal-title":"IEEE Trans Evol Comput"},{"key":"2143_CR9","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.knosys.2017.12.027","volume":"144","author":"S Wang","year":"2018","unstructured":"Wang S et al (2018) Convolutional neural network-based hidden Markov models for rolling element bearing fault identification. Knowl Based Syst 144:65\u201376","journal-title":"Knowl Based Syst"},{"issue":"4","key":"2143_CR10","doi-asserted-by":"crossref","first-page":"2983","DOI":"10.1007\/s00500-019-04076-2","volume":"24","author":"S Wang","year":"2020","unstructured":"Wang S, Xiang J (2020) A minimum entropy deconvolution-enhanced convolutional neural networks for fault diagnosis of axial piston pumps. Soft Comput 24(4):2983\u20132997","journal-title":"Soft Comput"},{"key":"2143_CR11","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.jocs.2017.02.012","volume":"25","author":"W-J Yan","year":"2018","unstructured":"Yan W-J, Chen Y-H (2018) Measuring dynamic micro-expressions via feature extraction methods. J Comput Sci 25:318\u2013326","journal-title":"J Comput Sci"},{"issue":"4","key":"2143_CR12","first-page":"1","volume":"40","author":"J Zhang","year":"2021","unstructured":"Zhang J et al (2021) ROSEFusion: random optimization for online dense reconstruction under fast camera motion. ACM Trans Graph (TOG) 40(4):1\u201317","journal-title":"ACM Trans Graph (TOG)"},{"issue":"5","key":"2143_CR13","doi-asserted-by":"crossref","first-page":"3597","DOI":"10.1109\/TII.2019.2952565","volume":"16","author":"B Cao","year":"2019","unstructured":"Cao B et al (2019) Multiobjective 3-D topology optimization of next-generation wireless data center network. IEEE Trans Ind Inform 16(5):3597\u20133605","journal-title":"IEEE Trans Ind Inform"},{"key":"2143_CR14","volume":"554","author":"J Cao","year":"2023","unstructured":"Cao J et al (2023) Reconstruction of full-field dynamic responses for large-scale structures using optimal sensor placement. J Sound Vib 554:117693","journal-title":"J Sound Vib"},{"issue":"8","key":"2143_CR15","doi-asserted-by":"crossref","first-page":"5309","DOI":"10.1109\/TII.2019.2961340","volume":"16","author":"B Cao","year":"2019","unstructured":"Cao B et al (2019) Security-aware industrial wireless sensor network deployment optimization. IEEE Trans Ind Inform 16(8):5309\u20135316","journal-title":"IEEE Trans Ind Inform"},{"issue":"1","key":"2143_CR16","doi-asserted-by":"crossref","first-page":"112","DOI":"10.26599\/TST.2022.9010065","volume":"29","author":"Q Wu","year":"2023","unstructured":"Wu Q et al (2023) Monte Carlo simulation-based robust workflow scheduling for spot instances in cloud environments. Tsinghua Sci Technol 29(1):112\u2013126","journal-title":"Tsinghua Sci Technol"},{"issue":"5","key":"2143_CR17","doi-asserted-by":"crossref","first-page":"8973","DOI":"10.1109\/JIOT.2023.3321673","volume":"11","author":"T Lyu","year":"2023","unstructured":"Lyu T et al (2023) Source selection and resource allocation in wireless-powered relay networks: an adaptive dynamic programming-based approach. IEEE Int Things J 11(5):8973\u20138988","journal-title":"IEEE Internet of Things Journal"},{"issue":"4","key":"2143_CR18","doi-asserted-by":"crossref","first-page":"2133","DOI":"10.1109\/TITS.2020.3040909","volume":"22","author":"B Cao","year":"2020","unstructured":"Cao B et al (2020) Diversified personalized recommendation optimization based on mobile data. IEEE Trans Intell Transp Syst 22(4):2133\u20132139","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"6","key":"2143_CR19","doi-asserted-by":"crossref","first-page":"4183","DOI":"10.1109\/TSC.2023.3311785","volume":"16","author":"Y Xie","year":"2023","unstructured":"Xie Y et al (2023) A two-stage estimation of distribution algorithm with heuristics for energy-aware cloud workflow scheduling. IEEE Trans Serv Comput 16(6):4183\u20134197","journal-title":"IEEE Trans Serv Comput"},{"key":"2143_CR20","volume":"235","author":"X Xu","year":"2021","unstructured":"Xu X, Wang C, Zhou P (2021) GVRP considered oil-gas recovery in refined oil distribution: from an environmental perspective. Int J Prod Econ 235:108078","journal-title":"Int J Prod Econ"},{"issue":"12","key":"2143_CR21","doi-asserted-by":"crossref","first-page":"15527","DOI":"10.1109\/TITS.2022.3183215","volume":"24","author":"J Mou","year":"2023","unstructured":"Mou J et al (2023) A machine learning approach for energy-efficient intelligent transportation scheduling problem in a real-world dynamic circumstances. IEEE Trans Intell Transp Syst 24(12):15527\u201315539","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"22","key":"2143_CR22","doi-asserted-by":"crossref","first-page":"6772","DOI":"10.1080\/00207543.2021.1887534","volume":"60","author":"X Xu","year":"2022","unstructured":"Xu X et al (2022) Multi-objective robust optimisation model for MDVRPLS in refined oil distribution. Int J Prod Res 60(22):6772\u20136792","journal-title":"Int J Prod Res"},{"issue":"11","key":"2143_CR23","first-page":"6599","volume":"22","author":"Z Xiao","year":"2023","unstructured":"Xiao Z et al (2023) Multi-objective parallel task offloading and content caching in D2D-aided MEC networks. IEEE Trans Mob Comput 22(11):6599\u20136615","journal-title":"IEEE Trans Mob Comput"},{"issue":"8","key":"2143_CR24","doi-asserted-by":"crossref","first-page":"837","DOI":"10.3390\/machines11080837","volume":"11","author":"S Li","year":"2023","unstructured":"Li S et al (2023) Hybrid method with parallel-factor theory, a support vector machine, and particle filter optimization for intelligent machinery failure identification. Machines 11(8):837","journal-title":"Machines"},{"issue":"5","key":"2143_CR25","doi-asserted-by":"crossref","first-page":"3099","DOI":"10.1109\/JIOT.2020.3033473","volume":"8","author":"B Cao","year":"2020","unstructured":"Cao B et al (2020) RFID reader anticollision based on distributed parallel particle swarm optimization. IEEE Internet Things J 8(5):3099\u20133107","journal-title":"IEEE Internet Things J"},{"key":"2143_CR26","volume":"114","author":"X Zhou","year":"2022","unstructured":"Zhou X et al (2022) Parameter adaptation-based ant colony optimization with dynamic hybrid mechanism. Eng Appl Artif Intell 114:105139","journal-title":"Eng Appl Artif Intell"},{"issue":"8","key":"2143_CR27","doi-asserted-by":"crossref","first-page":"735","DOI":"10.3139\/120.111378","volume":"61","author":"AR Yildiz","year":"2019","unstructured":"Yildiz AR et al (2019) A new hybrid Harris hawks-Nelder-Mead optimization algorithm for solving design and manufacturing problems. Mater Test 61(8):735\u2013743","journal-title":"Mater Test"},{"key":"2143_CR28","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.114864","volume":"177","author":"Y Yang","year":"2021","unstructured":"Yang Y et al (2021) Hunger games search: visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Syst Appl 177:114864","journal-title":"Expert Syst Appl"},{"key":"2143_CR29","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107389","volume":"165","author":"EH Houssein","year":"2023","unstructured":"Houssein EH et al (2023) Liver Cancer Algorithm: a novel bio-inspired optimizer. Comput Biol Med 165:107389","journal-title":"Comput Biol Med"},{"issue":"8","key":"2143_CR30","doi-asserted-by":"crossref","first-page":"7974","DOI":"10.1109\/TITS.2023.3268324","volume":"24","author":"B Zhu","year":"2023","unstructured":"Zhu B et al (2023) A critical scenario search method for intelligent vehicle testing based on the social cognitive optimization algorithm. IEEE Trans Intell Transp Sys 24(8):7974\u20137986","journal-title":"IEEE Trans Intell Transp Sys"},{"issue":"1","key":"2143_CR31","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1080\/00207721.2022.2153635","volume":"54","author":"H Chen","year":"2022","unstructured":"Chen H et al (2022) Slime mould algorithm: a comprehensive review of recent variants and applications. Int J Syst Sci 54(1):204\u2013235","journal-title":"Int J Syst Sci"},{"key":"2143_CR32","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.future.2020.03.055","volume":"111","author":"S Li","year":"2020","unstructured":"Li S et al (2020) Slime mould algorithm: a new method for stochastic optimization. Future Gener Comput Syst 111:300\u2013323","journal-title":"Future Gener Comput Syst"},{"key":"2143_CR33","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1016\/j.future.2019.02.028","volume":"97","author":"AA Heidari","year":"2019","unstructured":"Heidari AA et al (2019) Harris hawks optimization: algorithm and applications. Future Gener Comput Syst Int J Esci 97:849\u2013872","journal-title":"Future Gener Comput Syst Int J Esci"},{"issue":"3","key":"2143_CR34","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1007\/s42235-021-0050-y","volume":"18","author":"J Tu","year":"2021","unstructured":"Tu J et al (2021) The colony predation algorithm. J Bionic Eng 18(3):674\u2013710","journal-title":"J Bionic Eng"},{"key":"2143_CR35","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.115079","volume":"181","author":"I Ahmadianfar","year":"2021","unstructured":"Ahmadianfar I et al (2021) RUN beyond the metaphor: an efficient optimization algorithm based on Runge Kutta method. Expert Syst Appl 181:115079","journal-title":"Expert Syst Appl"},{"key":"2143_CR36","doi-asserted-by":"crossref","unstructured":"Ahmadianfar I et al (2022) INFO: an efficient optimization algorithm based on weighted mean of vectors. Expert Syst Appl 116516","DOI":"10.1016\/j.eswa.2022.116516"},{"key":"2143_CR37","doi-asserted-by":"crossref","first-page":"57743","DOI":"10.1109\/ACCESS.2021.3072831","volume":"9","author":"H-P Hsu","year":"2021","unstructured":"Hsu H-P, Wang C-N (2021) A hybrid approach combining improved shuffled frog-leaping algorithm with dynamic programming for disassembly process planning. IEEE Access 9:57743\u201357756","journal-title":"IEEE Access"},{"key":"2143_CR38","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2021.107085","volume":"102","author":"Y Huang","year":"2021","unstructured":"Huang Y, Shen X-N, You X (2021) A discrete shuffled frog-leaping algorithm based on heuristic information for traveling salesman problem. Appl Soft Comput 102:107085","journal-title":"Appl Soft Comput"},{"key":"2143_CR39","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1016\/j.ijepes.2014.07.073","volume":"64","author":"M Jadidoleslam","year":"2015","unstructured":"Jadidoleslam M, Ebrahimi A (2015) Reliability constrained generation expansion planning by a modified shuffled frog leaping algorithm. Int J Electr Power Energy Syst 64:743\u2013751","journal-title":"Int J Electr Power Energy Syst"},{"issue":"5","key":"2143_CR40","doi-asserted-by":"crossref","first-page":"4301","DOI":"10.1007\/s40747-022-00715-8","volume":"8","author":"Y Chen","year":"2022","unstructured":"Chen Y, Zhou A (2022) Multiobjective portfolio optimization via Pareto front evolution. Complex Intell Syst 8(5):4301\u20134317","journal-title":"Complex Intell Syst"},{"key":"2143_CR41","unstructured":"Zhang C, Zhou L, Li Y (2023) Pareto optimal reconfiguration planning and distributed parallel motion control of mobile modular robots. IEEE Trans Ind Electron 1\u201310"},{"issue":"1","key":"2143_CR42","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1007\/s42235-023-00436-9","volume":"21","author":"A Got","year":"2023","unstructured":"Got A et al (2023) Improved manta ray foraging optimizer-based SVM for feature selection problems: a medical case study. J Bionic Eng 21(1):409\u2013425","journal-title":"J Bionic Eng"},{"key":"2143_CR43","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.116511","volume":"194","author":"Y Chen","year":"2022","unstructured":"Chen Y et al (2022) Multi-threshold image segmentation using a multi-strategy shuffled frog leaping algorithm. Expert Syst Appl 194:116511","journal-title":"Expert Syst Appl"},{"issue":"6","key":"2143_CR44","doi-asserted-by":"crossref","first-page":"1656","DOI":"10.1109\/TSMCB.2012.2227469","volume":"43","author":"B Xue","year":"2013","unstructured":"Xue B, Zhang M, Browne WN (2013) Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE Trans Cybern 43(6):1656\u20131671","journal-title":"IEEE Trans Cybern"},{"issue":"8","key":"2143_CR45","doi-asserted-by":"crossref","first-page":"3155","DOI":"10.1007\/s12652-018-1031-9","volume":"10","author":"RA Ibrahim","year":"2019","unstructured":"Ibrahim RA et al (2019) Improved salp swarm algorithm based on particle swarm optimization for feature selection. J Ambient Intell Humaniz Comput 10(8):3155\u20133169","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"2143_CR46","doi-asserted-by":"crossref","first-page":"107804","DOI":"10.1016\/j.patcog.2020.107804","volume":"112","author":"X-F Song","year":"2021","unstructured":"Song X-F et al (2021) Feature selection using bare-bones particle swarm optimization with mutual information. Patt Recognit 112:107804","journal-title":"Patt Recognit"},{"issue":"9","key":"2143_CR47","doi-asserted-by":"crossref","first-page":"9573","DOI":"10.1109\/TCYB.2021.3061152","volume":"52","author":"X-F Song","year":"2021","unstructured":"Song X-F et al (2021) A fast hybrid feature selection based on correlation-guided clustering and particle swarm optimization for high-dimensional data. IEEE Trans Cybern 52(9):9573\u20139586","journal-title":"IEEE Trans Cybern"},{"key":"2143_CR48","volume":"106","author":"A-D Li","year":"2021","unstructured":"Li A-D, Xue B, Zhang M (2021) Improved binary particle swarm optimization for feature selection with new initialization and search space reduction strategies. Appl Soft Comput 106:107302","journal-title":"Appl Soft Comput"},{"key":"2143_CR49","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1016\/j.ijinfomgt.2018.12.001","volume":"50","author":"J Uthayakumar","year":"2020","unstructured":"Uthayakumar J et al (2020) Financial crisis prediction model using ant colony optimization. Int J Inf Manag 50:538\u2013556","journal-title":"Int J Inf Manag"},{"key":"2143_CR50","doi-asserted-by":"crossref","DOI":"10.1016\/j.swevo.2021.100892","volume":"64","author":"M Paniri","year":"2021","unstructured":"Paniri M, Dowlatshahi MB, Nezamabadi-pour H (2021) Ant-TD: ant colony optimization plus temporal difference reinforcement learning for multi-label feature selection. Swarm Evol Comput 64:100892","journal-title":"Swarm Evol Comput"},{"key":"2143_CR51","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.asoc.2018.10.017","volume":"74","author":"U Singh","year":"2019","unstructured":"Singh U, Singh SN (2019) A new optimal feature selection scheme for classification of power quality disturbances based on ant colony framework. Appl Soft Comput 74:216\u2013225","journal-title":"Appl Soft Comput"},{"key":"2143_CR52","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.geoderma.2018.07.004","volume":"333","author":"Y Zhang","year":"2019","unstructured":"Zhang Y et al (2019) Spectral features extraction for estimation of soil total nitrogen content based on modified ant colony optimization algorithm. Geoderma 333:23\u201334","journal-title":"Geoderma"},{"issue":"9","key":"2143_CR53","doi-asserted-by":"crossref","first-page":"2798","DOI":"10.1016\/j.patcog.2015.03.020","volume":"48","author":"S Tabakhi","year":"2015","unstructured":"Tabakhi S, Moradi P (2015) Relevance\u2013redundancy feature selection based on ant colony optimization. Pattern Recognit 48(9):2798\u20132811","journal-title":"Pattern Recognit"},{"key":"2143_CR54","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2019.105285","volume":"192","author":"M Paniri","year":"2020","unstructured":"Paniri M, Dowlatshahi MB, Nezamabadi-Pour H (2020) MLACO: a multi-label feature selection algorithm based on ant colony optimization. Knowl Based Syst 192:105285","journal-title":"Knowl Based Syst"},{"key":"2143_CR55","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1007\/s10462-020-09860-3","volume":"54","author":"M Abdel-Basset","year":"2021","unstructured":"Abdel-Basset M, Ding W, El-Shahat D (2021) A hybrid Harris Hawks optimization algorithm with simulated annealing for feature selection. Artif Intell Rev 54:593\u2013637","journal-title":"Artif Intell Rev"},{"issue":"Suppl 5","key":"2143_CR56","doi-asserted-by":"crossref","first-page":"4457","DOI":"10.1007\/s00366-021-01479-4","volume":"38","author":"J Too","year":"2022","unstructured":"Too J, Liang G, Chen H (2022) Memory-based Harris hawk optimization with learning agents: a feature selection approach. Eng Comput 38(Suppl 5):4457\u20134478","journal-title":"Eng Comput"},{"key":"2143_CR57","doi-asserted-by":"crossref","first-page":"3741","DOI":"10.1007\/s00366-020-01028-5","volume":"37","author":"Y Zhang","year":"2021","unstructured":"Zhang Y et al (2021) Boosted binary Harris hawks optimizer and feature selection. Eng Comput 37:3741\u20133770","journal-title":"Eng Comput"},{"key":"2143_CR58","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.114778","volume":"176","author":"K Hussain","year":"2021","unstructured":"Hussain K et al (2021) An efficient hybrid sine-cosine Harris hawks optimization for low and high-dimensional feature selection. Expert Syst Appl 176:114778","journal-title":"Expert Syst Appl"},{"key":"2143_CR59","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.117255","volume":"202","author":"W Long","year":"2022","unstructured":"Long W et al (2022) Lens-imaging learning Harris hawks optimizer for global optimization and its application to feature selection. Expert Syst Appl 202:117255","journal-title":"Expert Syst Appl"},{"key":"2143_CR60","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.ins.2019.08.040","volume":"507","author":"Y Zhang","year":"2020","unstructured":"Zhang Y et al (2020) Binary differential evolution with self-learning for multi-objective feature selection. Inf Sci 507:67\u201385","journal-title":"Inf Sci"},{"key":"2143_CR61","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.eswa.2016.06.004","volume":"62","author":"E Zorarpac\u0131","year":"2016","unstructured":"Zorarpac\u0131 E, \u00d6zel SA (2016) A hybrid approach of differential evolution and artificial bee colony for feature selection. Expert Syst Appl 62:91\u2013103","journal-title":"Expert Syst Appl"},{"key":"2143_CR62","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.asoc.2016.08.011","volume":"49","author":"Y Wan","year":"2016","unstructured":"Wan Y et al (2016) A feature selection method based on modified binary coded ant colony optimization algorithm. Appl Soft Comput 49:248\u2013258","journal-title":"Appl Soft Comput"},{"key":"2143_CR63","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.knosys.2017.02.013","volume":"123","author":"AK Das","year":"2017","unstructured":"Das AK, Das S, Ghosh A (2017) Ensemble feature selection using bi-objective genetic algorithm. Knowl Based Syst 123:116\u2013127","journal-title":"Knowl Based Syst"},{"key":"2143_CR64","volume":"260","author":"Y Hu","year":"2023","unstructured":"Hu Y et al (2023) A federated feature selection algorithm based on particle swarm optimization under privacy protection. Knowl Based Syst 260:110122","journal-title":"Knowl Based Syst"},{"key":"2143_CR65","volume":"641","author":"A-D Li","year":"2023","unstructured":"Li A-D, Xue B, Zhang M (2023) Multi-objective particle swarm optimization for key quality feature selection in complex manufacturing processes. Inf Sci 641:119062","journal-title":"Inf Sci"},{"key":"2143_CR66","doi-asserted-by":"crossref","DOI":"10.1016\/j.techfore.2023.122546","volume":"192","author":"A Dahou","year":"2023","unstructured":"Dahou A et al (2023) A social media event detection framework based on transformers and swarm optimization for public notification of crises and emergency management. Technol Forecast Soc Change 192:122546","journal-title":"Technol Forecast Soc Change"},{"key":"2143_CR67","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1109\/TEVC.2023.3254155","volume":"27","author":"L Li","year":"2023","unstructured":"Li L et al (2023) An evolutionary multitasking algorithm with multiple filtering for high-dimensional feature selection. IEEE Trans Evol Comput 27:802\u2013816","journal-title":"IEEE Trans Evol Comput"},{"key":"2143_CR68","volume":"77","author":"L Qu","year":"2023","unstructured":"Qu L et al (2023) Explicit and size-adaptive PSO-based feature selection for classification. Swarm Evol Comput 77:101249","journal-title":"Swarm Evol Comput"},{"issue":"4","key":"2143_CR69","doi-asserted-by":"crossref","first-page":"3929","DOI":"10.1007\/s12652-022-04462-z","volume":"14","author":"CN Aher","year":"2023","unstructured":"Aher CN, Jena AK (2023) Improved invasive weed bird swarm optimization algorithm (IWBSOA) enabled hybrid deep learning classifier for diabetic prediction. J Ambient Intell Humaniz Comput 14(4):3929\u20133945","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"6","key":"2143_CR70","doi-asserted-by":"crossref","first-page":"1896","DOI":"10.1109\/TEVC.2023.3238420","volume":"27","author":"B Ahadzadeh","year":"2023","unstructured":"Ahadzadeh B et al (2023) SFE: a simple, fast and efficient feature selection algorithm for high-dimensional data. IEEE Trans Evol Comput 27(6):1896\u20131911","journal-title":"IEEE Trans Evol Comput"},{"issue":"2","key":"2143_CR71","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.1007\/s00521-022-07836-8","volume":"35","author":"M Mafarja","year":"2023","unstructured":"Mafarja M et al (2023) An efficient high-dimensional feature selection approach driven by enhanced multi-strategy grey wolf optimizer for biological data classification. Neural Comput Appl 35(2):1749\u20131775","journal-title":"Neural Comput Appl"},{"key":"2143_CR72","first-page":"1","volume":"61","author":"Y Wan","year":"2023","unstructured":"Wan Y et al (2023) Adaptive multi-strategy particle swarm optimization for hyperspectral remote sensing image band selection. IEEE Trans Geosci Remote Sens 611\u201315","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"2143_CR73","volume":"117","author":"K Zhou","year":"2023","unstructured":"Zhou K et al (2023) Data preprocessing strategy in constructing convolutional neural network classifier based on constrained particle swarm optimization with fuzzy penalty function. Eng Appl Artif Intell 117:105580","journal-title":"Eng Appl Artif Intell"},{"key":"2143_CR74","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.inffus.2023.02.016","volume":"95","author":"L Sun","year":"2023","unstructured":"Sun L et al (2023) TFSFB: two-stage feature selection via fusing fuzzy multi-neighborhood rough set with binary whale optimization for imbalanced data. Inf Fusion 95:91\u2013108","journal-title":"Inf Fusion"},{"issue":"9","key":"2143_CR75","doi-asserted-by":"crossref","first-page":"483","DOI":"10.3390\/systems11090483","volume":"11","author":"X Liu","year":"2023","unstructured":"Liu X et al (2023) Adapting feature selection algorithms for the classification of Chinese texts. Systems 11(9):483","journal-title":"Systems"},{"issue":"6","key":"2143_CR76","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3136625","volume":"50","author":"J Li","year":"2017","unstructured":"Li J et al (2017) Feature selection: a data perspective. ACM Comput Surv (CSUR) 50(6):1\u201345","journal-title":"ACM Comput Surv (CSUR)"},{"key":"2143_CR77","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.neucom.2023.02.010","volume":"532","author":"H Su","year":"2023","unstructured":"Su H et al (2023) RIME: a physics-based optimization. Neurocomputing 532:183\u2013214","journal-title":"Neurocomputing"},{"key":"2143_CR78","volume":"165","author":"X Yu","year":"2023","unstructured":"Yu X et al (2023) Synergizing the enhanced RIME with fuzzy K-nearest neighbor for diagnose of pulmonary hypertension. Comput Biol Med 165:107408","journal-title":"Comput Biol Med"},{"issue":"11","key":"2143_CR79","doi-asserted-by":"crossref","first-page":"e16172","DOI":"10.1038\/lsa.2016.172","volume":"5","author":"T-J Cui","year":"2016","unstructured":"Cui T-J, Liu S, Li L-L (2016) Information entropy of coding metasurface. Light: Sci Appl 5(11):e16172","journal-title":"Light: Sci Appl"},{"issue":"7","key":"2143_CR80","doi-asserted-by":"crossref","first-page":"3182","DOI":"10.1109\/TIP.2016.2559803","volume":"25","author":"J Hou","year":"2016","unstructured":"Hou J, Gao H, Li X (2016) DSets-DBSCAN: a parameter-free clustering algorithm. IEEE Trans Image Process 25(7):3182\u20133193","journal-title":"IEEE Trans Image Process"},{"issue":"12","key":"2143_CR81","doi-asserted-by":"crossref","first-page":"5933","DOI":"10.1109\/TIP.2016.2616302","volume":"25","author":"J Shen","year":"2016","unstructured":"Shen J et al (2016) Real-time superpixel segmentation by DBSCAN clustering algorithm. IEEE Trans Image Process 25(12):5933\u20135942","journal-title":"IEEE Trans Image Process"},{"key":"2143_CR82","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1146\/annurev-astro-082214-122339","volume":"55","author":"S Sharma","year":"2017","unstructured":"Sharma S (2017) Markov chain Monte Carlo methods for Bayesian data analysis in astronomy. Annu Rev Astron Astrophys 55:213\u2013259","journal-title":"Annu Rev Astron Astrophys"},{"issue":"522","key":"2143_CR83","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1080\/01621459.2017.1294075","volume":"113","author":"A Bouchard-C\u00f4t\u00e9","year":"2018","unstructured":"Bouchard-C\u00f4t\u00e9 A, Vollmer SJ, Doucet A (2018) The bouncy particle sampler: a nonreversible rejection-free Markov chain Monte Carlo method. J Am Stat Assoc 113(522):855\u2013867","journal-title":"J Am Stat Assoc"},{"issue":"6","key":"2143_CR84","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3459665","volume":"54","author":"P Cunningham","year":"2021","unstructured":"Cunningham P, Delany SJ (2021) k-Nearest neighbour classifiers\u2014a tutorial. ACM Comput Surv (CSUR) 54(6):1\u201325","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"7","key":"2143_CR85","first-page":"2986","volume":"29","author":"C Wang","year":"2017","unstructured":"Wang C et al (2017) Feature selection based on neighborhood discrimination index. IEEE Trans Neural Netw Learn Syst 29(7):2986\u20132999","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2143_CR86","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.asoc.2015.10.009","volume":"38","author":"Y Lin","year":"2016","unstructured":"Lin Y et al (2016) Multi-label feature selection based on neighborhood mutual information. Appl Soft Comput 38:244\u2013256","journal-title":"Appl Soft Comput"},{"issue":"4","key":"2143_CR87","doi-asserted-by":"crossref","first-page":"2013","DOI":"10.1016\/j.eswa.2014.09.063","volume":"42","author":"J Lee","year":"2015","unstructured":"Lee J, Kim D-W (2015) Mutual information-based multi-label feature selection using interaction information. Expert Syst Appl 42(4):2013\u20132025","journal-title":"Expert Syst Appl"},{"issue":"10","key":"2143_CR88","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/JAS.2021.1004129","volume":"8","author":"J Tang","year":"2021","unstructured":"Tang J, Liu G, Pan Q (2021) A review on representative swarm intelligence algorithms for solving optimization problems: applications and trends. IEEE\/CAA J Autom Sin 8(10):1627\u20131643","journal-title":"IEEE\/CAA J Autom Sin"},{"key":"2143_CR89","doi-asserted-by":"crossref","unstructured":"Chakraborty A, Kar AK (2017) Swarm intelligence: a review of algorithms. In: Nature-inspired computing and optimization: theory and applications 10:475\u2013494","DOI":"10.1007\/978-3-319-50920-4_19"},{"issue":"3","key":"2143_CR90","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1109\/TII.2017.2786782","volume":"14","author":"A Slowik","year":"2017","unstructured":"Slowik A, Kwasnicka H (2017) Nature inspired methods and their industry applications\u2014swarm intelligence algorithms. IEEE Trans Ind Inform 14(3):1004\u20131015","journal-title":"IEEE Trans Ind Inform"},{"key":"2143_CR91","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.ins.2019.06.036","volume":"502","author":"SF Gal\u00e1n","year":"2019","unstructured":"Gal\u00e1n SF (2019) Comparative evaluation of region query strategies for DBSCAN clustering. Inf Sci 502:76\u201390","journal-title":"Inf Sci"},{"key":"2143_CR92","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s11263-016-0967-5","volume":"123","author":"S Sch\u00f6nborn","year":"2017","unstructured":"Sch\u00f6nborn S et al (2017) Markov chain Monte Carlo for automated face image analysis. Int J Comput Vis 123:160\u2013183","journal-title":"Int J Comput Vis"},{"issue":"7","key":"2143_CR93","first-page":"3523","volume":"44","author":"S Minaee","year":"2021","unstructured":"Minaee S et al (2021) Image segmentation using deep learning: a survey. IEEE Trans Pattern Anal Mach Intell 44(7):3523\u20133542","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2143_CR94","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1007\/s10462-017-9597-8","volume":"52","author":"A Yousif","year":"2019","unstructured":"Yousif A et al (2019) A survey on sentiment analysis of scientific citations. Artif Intell Rev 52:1805\u20131838","journal-title":"Artif Intell Rev"},{"issue":"5439","key":"2143_CR95","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1126\/science.286.5439.531","volume":"286","author":"TR Golub","year":"1999","unstructured":"Golub TR et al (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439):531\u2013537","journal-title":"Science"},{"issue":"2","key":"2143_CR96","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/S1535-6108(02)00030-2","volume":"1","author":"D Singh","year":"2002","unstructured":"Singh D et al (2002) Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1(2):203\u2013209","journal-title":"Cancer Cell"},{"issue":"1","key":"2143_CR97","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-15-1","volume":"15","author":"K-H Chen","year":"2014","unstructured":"Chen K-H et al (2014) Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm. BMC Bioinform 15(1):1\u201310","journal-title":"BMC Bioinform"},{"issue":"7","key":"2143_CR98","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1016\/j.compbiomed.2013.04.018","volume":"43","author":"Y Cui","year":"2013","unstructured":"Cui Y et al (2013) Sparse maximum margin discriminant analysis for feature extraction and gene selection on gene expression data. Comput Biol Med 43(7):933\u2013941","journal-title":"Comput Biol Med"},{"issue":"6","key":"2143_CR99","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1038\/89044","volume":"7","author":"J Khan","year":"2001","unstructured":"Khan J et al (2001) Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med 7(6):673\u2013679","journal-title":"Nat Med"},{"key":"2143_CR100","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2019.105277","volume":"191","author":"D Pelusi","year":"2020","unstructured":"Pelusi D et al (2020) An Improved Moth-Flame Optimization algorithm with hybrid search phase. Knowl Based Syst 191:105277","journal-title":"Knowl Based Syst"},{"key":"2143_CR101","doi-asserted-by":"crossref","first-page":"6617","DOI":"10.1007\/s00500-018-3310-y","volume":"23","author":"Z-J Teng","year":"2019","unstructured":"Teng Z-J, Lv J-L, Guo L-W (2019) An improved hybrid grey wolf optimization algorithm. Soft Comput 23:6617\u20136631","journal-title":"Soft Comput"},{"issue":"4","key":"2143_CR102","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1587\/transinf.2016EDP7512","volume":"100","author":"Z Song","year":"2017","unstructured":"Song Z et al (2017) Multiple chaos embedded gravitational search algorithm. IEICE Trans Inf Syst 100(4):888\u2013900","journal-title":"IEICE Trans Inf Syst"},{"key":"2143_CR103","volume":"135","author":"Z Liu","year":"2021","unstructured":"Liu Z et al (2021) A hybrid genetic-particle swarm algorithm based on multilevel neighbourhood structure for flexible job shop scheduling problem. Comput Oper Res 135:105431","journal-title":"Comput Oper Res"},{"issue":"3","key":"2143_CR104","doi-asserted-by":"crossref","DOI":"10.1111\/j.1468-0394.1992.tb00393.x","volume":"39","author":"P Sharma","year":"2022","unstructured":"Sharma P, Sharma K (2022) A novel quantum-inspired binary bat algorithm for leukocytes classification in blood smear. Expert Syst 39(3):e12813","journal-title":"Expert Syst"},{"key":"2143_CR105","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.neucom.2022.06.075","volume":"503","author":"Y Liu","year":"2022","unstructured":"Liu Y et al (2022) Simulated annealing-based dynamic step shuffled frog leaping algorithm: optimal performance design and feature selection. Neurocomputing 503:325\u2013362","journal-title":"Neurocomputing"},{"key":"2143_CR106","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.jare.2023.01.014","volume":"53","author":"L Peng","year":"2023","unstructured":"Peng L et al (2023) Hierarchical Harris hawks optimizer for feature selection. J Adv Res 53:261\u2013278","journal-title":"J Adv Res"},{"issue":"4","key":"2143_CR107","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S1364-0321(02)00005-9","volume":"6","author":"MA Leon","year":"2002","unstructured":"Leon MA, Kumar S, Bhattacharya S (2002) A comprehensive procedure for performance evaluation of solar food dryers. Renew Sustain Energy Rev 6(4):367\u2013393","journal-title":"Renew Sustain Energy Rev"},{"key":"2143_CR108","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1016\/j.rser.2015.12.284","volume":"58","author":"A Uihlein","year":"2016","unstructured":"Uihlein A, Magagna D (2016) Wave and tidal current energy\u2014a review of the current state of research beyond technology. Renew Sustain Energy Rev 58:1070\u20131081","journal-title":"Renew Sustain Energy Rev"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02143-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02143-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02143-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,17]],"date-time":"2024-08-17T08:29:58Z","timestamp":1723883398000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02143-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,24]]},"references-count":108,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["2143"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02143-1","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,24]]},"assertion":[{"value":"3 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2024","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 authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}