{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T07:25:53Z","timestamp":1772868353975,"version":"3.50.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2024,3,10]],"date-time":"2024-03-10T00:00:00Z","timestamp":1710028800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,10]],"date-time":"2024-03-10T00:00:00Z","timestamp":1710028800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["JP22H03643"],"award-info":[{"award-number":["JP22H03643"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002241","name":"Japan Science and Technology Agency","doi-asserted-by":"publisher","award":["JPMJSP2145"],"award-info":[{"award-number":["JPMJSP2145"]}],"id":[{"id":"10.13039\/501100002241","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002241","name":"Japan Science and Technology Agency","doi-asserted-by":"publisher","award":["JPMJFS2115"],"award-info":[{"award-number":["JPMJFS2115"]}],"id":[{"id":"10.13039\/501100002241","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":[[2024,9]]},"DOI":"10.1007\/s13042-024-02111-9","type":"journal-article","created":{"date-parts":[[2024,3,10]],"date-time":"2024-03-10T10:01:34Z","timestamp":1710064894000},"page":"3599-3617","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Feature selection with clustering probabilistic particle swarm optimization"],"prefix":"10.1007","volume":"15","author":[{"given":"Jinrui","family":"Gao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziqian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenyu","family":"Lei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rong-Long","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengwei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5042-3261","authenticated-orcid":false,"given":"Shangce","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,10]]},"reference":[{"issue":"1","key":"2111_CR1","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/j.patcog.2009.06.009","volume":"43","author":"IA Gheyas","year":"2010","unstructured":"Gheyas IA, Smith LS (2010) Feature subset selection in large dimensionality domains. Pattern Recognit 43(1):5\u201313. https:\/\/doi.org\/10.1016\/j.patcog.2009.06.009","journal-title":"Pattern Recognit"},{"issue":"6","key":"2111_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3136625","volume":"50","author":"J Li","year":"2017","unstructured":"Li J, Cheng K, Wang S, Morstatter F, Trevino RP, Tang J, Liu H (2017) Feature selection: a data perspective. ACM Comput Surv (CSUR) 50(6):1\u201345. https:\/\/doi.org\/10.1145\/3136625","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"Mar","key":"2111_CR3","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3(Mar):1157\u20131182","journal-title":"J Mach Learn Res"},{"issue":"1\u20134","key":"2111_CR4","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/S1088-467X(97)00008-5","volume":"1","author":"M Dash","year":"1997","unstructured":"Dash M, Liu H (1997) Feature selection for classification. Intell Data Anal 1(1\u20134):131\u2013156. https:\/\/doi.org\/10.1016\/S1088-467X(97)00008-5","journal-title":"Intell Data Anal"},{"key":"2111_CR5","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.neucom.2017.11.077","volume":"300","author":"J Cai","year":"2018","unstructured":"Cai J, Luo J, Wang S, Yang S (2018) Feature selection in machine learning: a new perspective. Neurocomputing 300:70\u201379. https:\/\/doi.org\/10.1016\/j.neucom.2017.11.077","journal-title":"Neurocomputing"},{"key":"2111_CR6","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.ins.2019.08.040","volume":"507","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Gong D-W, Gao X-Z, Tian T, Sun X (2020) Binary differential evolution with self-learning for multi-objective feature selection. Inf Sci 507:67\u201385. https:\/\/doi.org\/10.1016\/j.ins.2019.08.040","journal-title":"Inf Sci"},{"issue":"6","key":"2111_CR7","doi-asserted-by":"publisher","first-page":"4221","DOI":"10.1109\/TCYB.2020.3025205","volume":"52","author":"F Nie","year":"2020","unstructured":"Nie F, Wang Z, Tian L, Wang R, Li X (2020) Subspace sparse discriminative feature selection. IEEE Trans Cybern 52(6):4221\u20134233. https:\/\/doi.org\/10.1109\/TCYB.2020.3025205","journal-title":"IEEE Trans Cybern"},{"key":"2111_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115895","volume":"187","author":"A Alsahaf","year":"2022","unstructured":"Alsahaf A, Petkov N, Shenoy V, Azzopardi G (2022) A framework for feature selection through boosting. Expert Syst Appl 187:115895. https:\/\/doi.org\/10.1016\/j.eswa.2021.115895","journal-title":"Expert Syst Appl"},{"issue":"2","key":"2111_CR9","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1007\/s10462-019-09682-y","volume":"53","author":"S Solorio-Fern\u00e1ndez","year":"2020","unstructured":"Solorio-Fern\u00e1ndez S, Carrasco-Ochoa JA, Mart\u00ednez-Trinidad JF (2020) A review of unsupervised feature selection methods. Artif Intell Rev 53(2):907\u2013948. https:\/\/doi.org\/10.1007\/s10462-019-09682-y","journal-title":"Artif Intell Rev"},{"issue":"1","key":"2111_CR10","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.compeleceng.2013.11.024","volume":"40","author":"G Chandrashekar","year":"2014","unstructured":"Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Electr Eng 40(1):16\u201328. https:\/\/doi.org\/10.1016\/j.compeleceng.2013.11.024","journal-title":"Comput Electr Eng"},{"key":"2111_CR11","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1109\/TSMC.1976.5408784","volume":"4","author":"SA Dudani","year":"1976","unstructured":"Dudani SA (1976) The distance-weighted k-nearest-neighbor rule. IEEE Trans Syst Man Cybern 4:325\u2013327. https:\/\/doi.org\/10.1109\/TSMC.1976.5408784","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"2111_CR12","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1023\/A:1018628609742","volume":"9","author":"JA Suykens","year":"1999","unstructured":"Suykens JA, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9:293\u2013300. https:\/\/doi.org\/10.1023\/A:1018628609742","journal-title":"Neural Process Lett"},{"key":"2111_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2020.100663","volume":"54","author":"BH Nguyen","year":"2020","unstructured":"Nguyen BH, Xue B, Zhang M (2020) A survey on swarm intelligence approaches to feature selection in data mining. Swarm Evol Comput 54:100663. https:\/\/doi.org\/10.1016\/j.swevo.2020.100663","journal-title":"Swarm Evol Comput"},{"issue":"6","key":"2111_CR14","doi-asserted-by":"publisher","first-page":"1656","DOI":"10.1109\/TSMCB.2012.2227469","volume":"43","author":"B Xue","year":"2012","unstructured":"Xue B, Zhang M, Browne WN (2012) Particle swarm optimization for feature selection in classification: A multi-objective approach. IEEE Trans Cybern 43(6):1656\u20131671. https:\/\/doi.org\/10.1109\/TSMCB.2012.2227469","journal-title":"IEEE Trans Cybern"},{"issue":"9","key":"2111_CR15","doi-asserted-by":"publisher","first-page":"1100","DOI":"10.1109\/T-C.1971.223410","volume":"100","author":"AW Whitney","year":"1971","unstructured":"Whitney AW (1971) A direct method of nonparametric measurement selection. IEEE Trans Comput 100(9):1100\u20131103. https:\/\/doi.org\/10.1109\/T-C.1971.223410","journal-title":"IEEE Trans Comput"},{"issue":"1","key":"2111_CR16","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1109\/TIT.1963.1057810","volume":"9","author":"T Marill","year":"1963","unstructured":"Marill T, Green D (1963) On the effectiveness of receptors in recognition systems. IEEE Trans Inf Theory 9(1):11\u201317. https:\/\/doi.org\/10.1109\/TIT.1963.1057810","journal-title":"IEEE Trans Inf Theory"},{"issue":"11","key":"2111_CR17","doi-asserted-by":"publisher","first-page":"1119","DOI":"10.1016\/0167-8655(94)90127-9","volume":"15","author":"P Pudil","year":"1994","unstructured":"Pudil P, Novovi\u010dov\u00e1 J, Kittler J (1994) Floating search methods in feature selection. Pattern Recognit Lett 15(11):1119\u20131125. https:\/\/doi.org\/10.1016\/0167-8655(94)90127-9","journal-title":"Pattern Recognit Lett"},{"key":"2111_CR18","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1016\/j.ins.2017.08.047","volume":"418","author":"Y Zhang","year":"2017","unstructured":"Zhang Y, Song X-F, Gong D (2017) A return-cost-based binary firefly algorithm for feature selection. Inf Sci 418:561\u2013574. https:\/\/doi.org\/10.1016\/j.ins.2017.08.047","journal-title":"Inf Sci"},{"key":"2111_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2021.104210","volume":"100","author":"M Rostami","year":"2021","unstructured":"Rostami M, Berahmand K, Nasiri E, Forouzandeh S (2021) Review of swarm intelligence-based feature selection methods. Eng Appl Artif Intell 100:104210. https:\/\/doi.org\/10.1016\/j.engappai.2021.104210","journal-title":"Eng Appl Artif Intell"},{"key":"2111_CR20","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.neucom.2017.04.053","volume":"260","author":"MM Mafarja","year":"2017","unstructured":"Mafarja MM, Mirjalili S (2017) Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260:302\u2013312. https:\/\/doi.org\/10.1016\/j.neucom.2017.04.053","journal-title":"Neurocomputing"},{"key":"2111_CR21","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1016\/j.asoc.2016.08.011","volume":"49","author":"Y Wan","year":"2016","unstructured":"Wan Y, Wang M, Ye Z, Lai X (2016) A feature selection method based on modified binary coded ant colony optimization algorithm. Appl Soft Comput 49:248\u2013258. https:\/\/doi.org\/10.1016\/j.asoc.2016.08.011","journal-title":"Appl Soft Comput"},{"key":"2111_CR22","doi-asserted-by":"publisher","first-page":"1391","DOI":"10.1007\/s00521-018-3612-0","volume":"31","author":"O AlFarraj","year":"2019","unstructured":"AlFarraj O, AlZubi A, Tolba A (2019) Optimized feature selection algorithm based on fireflies with gravitational ant colony algorithm for big data predictive analytics. Neural Comput Appl 31:1391\u20131403. https:\/\/doi.org\/10.1007\/s00521-018-3612-0","journal-title":"Neural Comput Appl"},{"key":"2111_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2021.104288","volume":"212","author":"NA Al-Thanoon","year":"2021","unstructured":"Al-Thanoon NA, Algamal ZY, Qasim OS (2021) Feature selection based on a crow search algorithm for big data classification. Chemom Intell Lab Syst 212:104288. https:\/\/doi.org\/10.1016\/j.chemolab.2021.104288","journal-title":"Chemom Intell Lab Syst"},{"key":"2111_CR24","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1016\/j.jocs.2017.07.018","volume":"25","author":"LM Abualigah","year":"2018","unstructured":"Abualigah LM, Khader AT, Hanandeh ES (2018) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J Comput Sci 25:456\u2013466. https:\/\/doi.org\/10.1016\/j.jocs.2017.07.018","journal-title":"J Comput Sci"},{"key":"2111_CR25","doi-asserted-by":"publisher","first-page":"4287","DOI":"10.1007\/s00521-017-3317-9","volume":"31","author":"RK Huda","year":"2019","unstructured":"Huda RK, Banka H (2019) Efficient feature selection and classification algorithm based on PSO and rough sets. Neural Comput Appl 31:4287\u20134303. https:\/\/doi.org\/10.1007\/s00521-017-3317-9","journal-title":"Neural Comput Appl"},{"key":"2111_CR26","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1016\/j.eswa.2018.07.013","volume":"113","author":"M Amoozegar","year":"2018","unstructured":"Amoozegar M, Minaei-Bidgoli B (2018) Optimizing multi-objective PSO based feature selection method using a feature elitism mechanism. Expert Syst Appl 113:499\u2013514. https:\/\/doi.org\/10.1016\/j.eswa.2018.07.013","journal-title":"Expert Syst Appl"},{"issue":"1","key":"2111_CR27","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1587\/transinf.2021EDL8053","volume":"105","author":"J Li","year":"2022","unstructured":"Li J, Yang L, Yi J, Yang H, Todo Y, Gao S (2022) A simple but efficient ranking-based differential evolution. IEICE Trans Inf Syst 105(1):189\u2013192. https:\/\/doi.org\/10.1587\/transinf.2021EDL8053","journal-title":"IEICE Trans Inf Syst"},{"key":"2111_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110064","volume":"136","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Gao S, Cai P, Lei Z, Wang Y (2023) Information entropy-based differential evolution with extremely randomized trees and LightGBM for protein structural class prediction. Appl Soft Comput 136:110064. https:\/\/doi.org\/10.1016\/j.asoc.2023.110064","journal-title":"Appl Soft Comput"},{"issue":"1","key":"2111_CR29","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1109\/TSC.2015.2439695","volume":"9","author":"S Fong","year":"2015","unstructured":"Fong S, Wong R, Vasilakos AV (2015) Accelerated PSO swarm search feature selection for data stream mining big data. IEEE Trans Serv Comput 9(1):33\u201345. https:\/\/doi.org\/10.1109\/TSC.2015.2439695","journal-title":"IEEE Trans Serv Comput"},{"key":"2111_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2022.116174","volume":"269","author":"Z Lei","year":"2022","unstructured":"Lei Z, Gao S, Wang Y, Yu Y, Guo L (2022) An adaptive replacement strategy-incorporated particle swarm optimizer for wind farm layout optimization. Energy Convers Manag 269:116174. https:\/\/doi.org\/10.1016\/j.enconman.2022.116174","journal-title":"Energy Convers Manag"},{"key":"2111_CR31","doi-asserted-by":"publisher","first-page":"3155","DOI":"10.1007\/s12652-018-1031-9","volume":"10","author":"RA Ibrahim","year":"2019","unstructured":"Ibrahim RA, Ewees AA, Oliva D, Abd Elaziz M, Lu S (2019) Improved salp swarm algorithm based on particle swarm optimization for feature selection. J Ambient Intell Humaniz Comput 10:3155\u20133169. https:\/\/doi.org\/10.1007\/s12652-018-1031-9","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"2111_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106966","volume":"222","author":"D Paul","year":"2021","unstructured":"Paul D, Jain A, Saha S, Mathew J (2021) Multi-objective PSO based online feature selection for multi-label classification. Knowl Based Syst 222:106966. https:\/\/doi.org\/10.1016\/j.knosys.2021.106966","journal-title":"Knowl Based Syst"},{"issue":"5","key":"2111_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3340848","volume":"13","author":"Y Xue","year":"2019","unstructured":"Xue Y, Xue B, Zhang M (2019) Self-adaptive particle swarm optimization for large-scale feature selection in classification. ACM Trans Knowl Discov Data (TKDD) 13(5):1\u201327. https:\/\/doi.org\/10.1145\/3340848","journal-title":"ACM Trans Knowl Discov Data (TKDD)"},{"key":"2111_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109420","volume":"127","author":"Y Xue","year":"2022","unstructured":"Xue Y, Cai X, Neri F (2022) A multi-objective evolutionary algorithm with interval based initialization and self-adaptive crossover operator for large-scale feature selection in classification. Appl Soft Comput 127:109420. https:\/\/doi.org\/10.1016\/j.asoc.2022.109420","journal-title":"Appl Soft Comput"},{"issue":"2","key":"2111_CR35","doi-asserted-by":"publisher","first-page":"874","DOI":"10.1109\/TCYB.2020.3015756","volume":"51","author":"Y Hu","year":"2020","unstructured":"Hu Y, Zhang Y, Gong D (2020) Multiobjective particle swarm optimization for feature selection with fuzzy cost. IEEE Trans Cybern 51(2):874\u2013888. https:\/\/doi.org\/10.1109\/TCYB.2020.3015756","journal-title":"IEEE Trans Cybern"},{"issue":"2","key":"2111_CR36","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1109\/TETCI.2021.3074147","volume":"6","author":"Y Xue","year":"2021","unstructured":"Xue Y, Tang Y, Xu X, Liang J, Neri F (2021) Multi-objective feature selection with missing data in classification. IEEE Trans Emerg Top Comput Intell 6(2):355\u2013364. https:\/\/doi.org\/10.1109\/TETCI.2021.3074147","journal-title":"IEEE Trans Emerg Top Comput Intell"},{"key":"2111_CR37","doi-asserted-by":"publisher","unstructured":"Du K-L, Swamy M, Du K-L, Swamy M (2016) Particle swarm optimization. In: Search and optimization by metaheuristics: techniques and algorithms inspired by nature, pp 153\u2013173. https:\/\/doi.org\/10.1007\/978-3-319-41192-7_9","DOI":"10.1007\/978-3-319-41192-7_9"},{"issue":"2","key":"2111_CR38","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1016\/S0031-3203(02)00060-2","volume":"36","author":"A Likas","year":"2003","unstructured":"Likas A, Vlassis N, Verbeek JJ (2003) The global k-means clustering algorithm. Pattern Recognit 36(2):451\u2013461. https:\/\/doi.org\/10.1016\/S0031-3203(02)00060-2","journal-title":"Pattern Recognit"},{"key":"2111_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106341","volume":"93","author":"R Guha","year":"2020","unstructured":"Guha R, Ghosh M, Chakrabarti A, Sarkar R, Mirjalili S (2020) Introducing clustering based population in binary gravitational search algorithm for feature selection. Appl Soft Comput 93:106341. https:\/\/doi.org\/10.1016\/j.asoc.2020.106341","journal-title":"Appl Soft Comput"},{"key":"2111_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109464","volume":"128","author":"R Pramanik","year":"2022","unstructured":"Pramanik R, Sarkar S, Sarkar R (2022) An adaptive and altruistic PSO-based deep feature selection method for pneumonia detection from chest X-rays. Appl Soft Comput 128:109464. https:\/\/doi.org\/10.1016\/j.asoc.2022.109464","journal-title":"Appl Soft Comput"},{"issue":"21","key":"2111_CR41","doi-asserted-by":"publisher","first-page":"19377","DOI":"10.1007\/s00521-022-07522-9","volume":"34","author":"R Alwajih","year":"2022","unstructured":"Alwajih R, Abdulkadir SJ, Al Hussian H, Aziz N, Al-Tashi Q, Mirjalili S, Alqushaibi A (2022) Hybrid binary whale with harris hawks for feature selection. Neural Comput Appl 34(21):19377\u201319395. https:\/\/doi.org\/10.1007\/s00521-022-07522-9","journal-title":"Neural Comput Appl"},{"key":"2111_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113353","volume":"152","author":"H Liu","year":"2020","unstructured":"Liu H, Zhang X-W, Tu L-P (2020) A modified particle swarm optimization using adaptive strategy. Expert Syst Appl 152:113353. https:\/\/doi.org\/10.1016\/j.eswa.2020.113353","journal-title":"Expert Syst Appl"},{"issue":"3","key":"2111_CR43","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1109\/TEVC.2018.2869405","volume":"23","author":"B Tran","year":"2018","unstructured":"Tran B, Xue B, Zhang M (2018) Variable-length particle swarm optimization for feature selection on high-dimensional classification. IEEE Trans Evol Comput 23(3):473\u2013487. https:\/\/doi.org\/10.1109\/TEVC.2018.2869405","journal-title":"IEEE Trans Evol Comput"},{"key":"2111_CR44","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.asoc.2017.11.006","volume":"62","author":"M Mafarja","year":"2018","unstructured":"Mafarja M, Mirjalili S (2018) Whale optimization approaches for wrapper feature selection. Appl Soft Comput 62:441\u2013453. https:\/\/doi.org\/10.1016\/j.asoc.2017.11.006","journal-title":"Appl Soft Comput"},{"key":"2111_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109874","volume":"256","author":"Z Wang","year":"2022","unstructured":"Wang Z, Gao S, Zhang Y, Guo L (2022) Symmetric uncertainty-incorporated probabilistic sequence-based ant colony optimization for feature selection in classification. Knowl Based Syst 256:109874. https:\/\/doi.org\/10.1016\/j.knosys.2022.109874","journal-title":"Knowl Based Syst"},{"key":"2111_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108789","volume":"248","author":"AM Khalid","year":"2022","unstructured":"Khalid AM, Hamza HM, Mirjalili S, Hosny KM (2022) BCOVIDOA: a novel binary coronavirus disease optimization algorithm for feature selection. Knowl Based Syst 248:108789. https:\/\/doi.org\/10.1016\/j.knosys.2022.108789","journal-title":"Knowl Based Syst"},{"issue":"18","key":"2111_CR47","doi-asserted-by":"publisher","first-page":"2378","DOI":"10.1016\/j.fss.2007.12.023","volume":"159","author":"A Fern\u00e1ndez","year":"2008","unstructured":"Fern\u00e1ndez A, Garc\u00eda S, Jesus MJ, Herrera F (2008) A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets. Fuzzy Sets Syst 159(18):2378\u20132398. https:\/\/doi.org\/10.1016\/j.fss.2007.12.023","journal-title":"Fuzzy Sets Syst"},{"key":"2111_CR48","doi-asserted-by":"publisher","unstructured":"Gon\u00e7alves EC, Plastino A, Freitas AA (2013) A genetic algorithm for optimizing the label ordering in multi-label classifier chains. In: 2013 IEEE 25th international conference on tools with artificial intelligence. IEEE, pp 469\u2013476. https:\/\/doi.org\/10.1109\/ICTAI.2013.76","DOI":"10.1109\/ICTAI.2013.76"},{"key":"2111_CR49","unstructured":"Tsoumakas G, Katakis I, Vlahavas I (2008) Effective and efficient multilabel classification in domains with large number of labels. In: Proceedings of ECML\/PKDD 2008 workshop on mining multidimensional data (MMD\u201908), vol 21, pp 53\u201359"},{"key":"2111_CR50","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.asoc.2018.02.051","volume":"67","author":"S Maldonado","year":"2018","unstructured":"Maldonado S, L\u00f3pez J (2018) Dealing with high-dimensional class-imbalanced datasets: embedded feature selection for SVM classification. Appl Soft Comput 67:94\u2013105","journal-title":"Appl Soft Comput"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02111-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02111-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02111-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,17]],"date-time":"2024-08-17T08:18:22Z","timestamp":1723882702000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02111-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,10]]},"references-count":50,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["2111"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02111-9","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,10]]},"assertion":[{"value":"14 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 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 whose names are listed immediately below certify that they have no affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers\u2019 bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript. The authors have no relevant financial or non-financial interests to disclose","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}