乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T05:45:13Z","timestamp":1744004713878,"version":"3.37.3"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T00:00:00Z","timestamp":1683504000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T00:00:00Z","timestamp":1683504000000},"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":["Soft Comput"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s00500-023-08341-3","type":"journal-article","created":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T15:15:57Z","timestamp":1683558957000},"page":"1445-1457","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Feature selection based on correlation label and B-R belief function (FSCLBF) in multi-label data"],"prefix":"10.1007","volume":"28","author":[{"given":"Zahra","family":"Mehravaran","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6493-0539","authenticated-orcid":false,"given":"Javad","family":"Hamidzadeh","sequence":"additional","affiliation":[]},{"given":"Reza","family":"Monsefi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,8]]},"reference":[{"key":"8341_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eij.2020.08.004","author":"N Aljedani","year":"2020","unstructured":"Aljedani N, Alotaibi R, Taileb M (2020) HMATC: Hierarchical multi-label Arabic text classification model using machine learning. Egypt Informatics J. https:\/\/doi.org\/10.1016\/j.eij.2020.08.004","journal-title":"Egypt Informatics J"},{"key":"8341_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2020.08.004","volume":"547","author":"AA Bidgoli","year":"2021","unstructured":"Bidgoli AA, Ebrahimpour-Komleh H, Rahnamayan S, Asilian Bidgoli A, Ebrahimpour-Komleh H, Rahnamayan S (2021) Reference-point-based multi-objective optimization algorithm with opposition-based voting scheme for multi-label feature selection. Inf. Sci. (Ny) 547:1\u201317. https:\/\/doi.org\/10.1016\/j.ins.2020.08.004","journal-title":"Inf. Sci. (Ny)"},{"issue":"9","key":"8341_CR3","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1016\/j.patcog.2004.03.009","volume":"37","author":"MR Boutell","year":"2004","unstructured":"Boutell MR, Luo J, Shen X, Brown CM (2004) Learning multi-label scene classification. Pattern Recognit 37(9):1757\u20131771. https:\/\/doi.org\/10.1016\/j.patcog.2004.03.009","journal-title":"Pattern Recognit"},{"key":"8341_CR4","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1016\/j.ins.2019.10.022","volume":"512","author":"X Che","year":"2020","unstructured":"Che X, Chen D, Mi J (2020) A novel approach for learning label correlation with application to feature selection of multi-label data. Inf Sci (ny) 512:795\u2013812. https:\/\/doi.org\/10.1016\/j.ins.2019.10.022","journal-title":"Inf Sci (ny)"},{"key":"8341_CR5","doi-asserted-by":"publisher","first-page":"133565","DOI":"10.1109\/ACCESS.2020.3010314","volume":"8","author":"P Chen","year":"2020","unstructured":"Chen P, Lin M, Liu J (2020) Multi-label attribute reduction based on variable precision fuzzy neighborhood rough set. IEEE Access 8:133565\u2013133576. https:\/\/doi.org\/10.1109\/ACCESS.2020.3010314","journal-title":"IEEE Access"},{"issue":"1","key":"8341_CR6","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1007\/s10489-020-01807-z","volume":"51","author":"Y-NN Chen","year":"2021","unstructured":"Chen Y-NN, Weng W, Wu S-XX, Chen B-HH, Fan Y-LL, Liu J-HH (2021) An efficient stacking model with label selection for multi-label classification. Appl Intell 51(1):308\u2013325. https:\/\/doi.org\/10.1007\/s10489-020-01807-z","journal-title":"Appl Intell"},{"key":"8341_CR7","doi-asserted-by":"publisher","unstructured":"Dahiya K et al. DeepXML: a Deep extreme multi-label learning framework applied to short text documents. in Proceedings of the 14th ACM International Conference on Web Search and Data Mining, (2021)., vol. 1, no. 1, pp. 31\u201339, https:\/\/doi.org\/10.1145\/3437963.3441810.","DOI":"10.1145\/3437963.3441810"},{"key":"8341_CR8","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/978-3-642-01536-6_8","volume":"205","author":"ACPLF de Carvalho","year":"2009","unstructured":"de Carvalho ACPLF, Freitas AA (2009) A tutorial on multi-label classification techniques. Stud Comput Intell 205:177\u2013195. https:\/\/doi.org\/10.1007\/978-3-642-01536-6_8","journal-title":"Stud Comput Intell"},{"key":"8341_CR9","doi-asserted-by":"crossref","unstructured":"Dokeroglu T, Deniz A, and Kiziloz HE. A comprehensive survey on recent metaheuristics for feature selection. Neurocomputing, (2022).","DOI":"10.1016\/j.neucom.2022.04.083"},{"issue":"18","key":"8341_CR11","doi-asserted-by":"publisher","first-page":"14039","DOI":"10.1007\/s00500-020-04780-4","volume":"24","author":"X Fan","year":"2020","unstructured":"Fan X, Chen Q, Qiao Z, Wang C, Ten M (2020) Attribute reduction for multi-label classification based on labels of positive region. Soft Comput 24(18):14039\u201314049. https:\/\/doi.org\/10.1007\/s00500-020-04780-4","journal-title":"Soft Comput"},{"key":"8341_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106365","author":"A Hashemi","year":"2020","unstructured":"Hashemi A, Dowlatshahi MB, Nezamabadi-pour H (2020) MFS-MCDM: Multi-label feature selection using multi-criteria decision making. Knowl Based Syst. https:\/\/doi.org\/10.1016\/j.knosys.2020.106365","journal-title":"Knowl Based Syst"},{"issue":"2","key":"8341_CR13","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1007\/s13042-020-01180-w","volume":"12","author":"A Hashemi","year":"2021","unstructured":"Hashemi A, Dowlatshahi MB, Nezamabadi-Pour H (2021) A bipartite matching-based feature selection for multi-label learning. Int J Mach Learn Cybern 12(2):459\u2013475","journal-title":"Int J Mach Learn Cybern"},{"key":"8341_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106126","volume":"203","author":"J Hu","year":"2020","unstructured":"Hu J, Li Y, Gao W, Zhang P (2020) Robust multi-label feature selection with dual-graph regularization. Knowl Based Syst. 203:106126. https:\/\/doi.org\/10.1016\/j.knosys.2020.106126","journal-title":"Knowl Based Syst."},{"key":"8341_CR15","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.neucom.2016.12.073","volume":"257","author":"J Huang","year":"2017","unstructured":"Huang J, Li G, Wang S, Xue Z, Huang Q (2017) Multi-label classification by exploiting local positive and negative pairwise label correlation. Neurocomputing 257:164\u2013174. https:\/\/doi.org\/10.1016\/j.neucom.2016.12.073","journal-title":"Neurocomputing"},{"issue":"1","key":"8341_CR16","doi-asserted-by":"publisher","first-page":"17","DOI":"10.25073\/2588-1086\/vnucsce.238","volume":"36","author":"PT Huyen","year":"2020","unstructured":"Huyen PT, Thuan H (2020) A new Feature Reduction Algorithm Based on Fuzzy Rough Relation for the Multi-label Classification. VNU J Sci Comput Sci Commun Eng 36(1):17\u201324","journal-title":"VNU J Sci Comput Sci Commun Eng"},{"issue":"15","key":"8341_CR17","doi-asserted-by":"publisher","first-page":"11425","DOI":"10.1007\/s00500-019-04605-z","volume":"24","author":"SMZ Kashani","year":"2020","unstructured":"Kashani SMZ, Hamidzadeh J (2020) Feature selection by using privacy-preserving of recommendation systems based on collaborative filtering and mutual trust in social networks. Soft Comput 24(15):11425\u201311440. https:\/\/doi.org\/10.1007\/s00500-019-04605-z","journal-title":"Soft Comput"},{"issue":"2","key":"8341_CR18","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1240","volume":"8","author":"S Kashef","year":"2018","unstructured":"Kashef S, Nezamabadi-pour H, Nikpour B, Nezamabadi-pour H, Nikpour B (2018) Multilabel feature selection: a comprehensive review and guiding experiments. Wiley Interdiscip Rev Data Min Knowl Discov 8(2):e1240. https:\/\/doi.org\/10.1002\/widm.1240","journal-title":"Wiley Interdiscip Rev Data Min Knowl Discov"},{"key":"8341_CR26","doi-asserted-by":"publisher","unstructured":"Kelidari M, Hamidzadeh J, Feature selection by using chaotic cuckoo optimization algorithm with levy flight, opposition-based learning and disruption operator. Soft Comput. vol. 25, no. 4, pp. 2911\u20132933, (2021),https:\/\/doi.org\/10.1007\/s00500-020-05349-x.","DOI":"10.1007\/s00500-020-05349-x"},{"key":"8341_CR19","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1016\/j.ins.2016.07.008","volume":"367","author":"H Li","year":"2016","unstructured":"Li H, Li D, Zhai Y, Wang S, Zhang J (2016) A novel attribute reduction approach for multi-label data based on rough set theory. Inf Sci (ny) 367:827\u2013847. https:\/\/doi.org\/10.1016\/j.ins.2016.07.008","journal-title":"Inf Sci (ny)"},{"key":"8341_CR20","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1145\/3136625","volume":"50","author":"J Li","year":"2017","unstructured":"Li J et al (2017) Feature selectin: A data perspective. ACM Comput Surv 50:6. https:\/\/doi.org\/10.1145\/3136625","journal-title":"ACM Comput Surv"},{"key":"8341_CR21","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.neucom.2018.08.065","volume":"318","author":"Y Li","year":"2018","unstructured":"Li Y, Lin Y, Liu J, Weng W, Shi Z, Wu S (2018) Feature selection for multi-label learning based on kernelized fuzzy rough sets. Neurocomputing 318:271\u2013286. https:\/\/doi.org\/10.1016\/j.neucom.2018.08.065","journal-title":"Neurocomputing"},{"key":"8341_CR22","doi-asserted-by":"crossref","unstructured":"Li Y, Wei S, Liu X, Zhang Z (2021) A novel robust fuzzy rough set model for feature selection. Complexity, vol. 2021","DOI":"10.1155\/2021\/6685396"},{"key":"8341_CR23","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.knosys.2018.04.004","volume":"152","author":"Y Lin","year":"2018","unstructured":"Lin Y, Li Y, Wang C, Chen J (2018) Attribute reduction for multi-label learning with fuzzy rough set. Knowl Based Syst 152:51\u201361. https:\/\/doi.org\/10.1016\/j.knosys.2018.04.004","journal-title":"Knowl Based Syst"},{"issue":"6","key":"8341_CR24","doi-asserted-by":"publisher","first-page":"1209","DOI":"10.1109\/TCYB.2014.2347372","volume":"45","author":"Y Liu","year":"2014","unstructured":"Liu Y, Tang F, Zeng Z (2014) Feature selection based on dependency margin. IEEE Trans Cybern 45(6):1209\u20131221","journal-title":"IEEE Trans Cybern"},{"issue":"3","key":"8341_CR25","doi-asserted-by":"publisher","first-page":"518","DOI":"10.1109\/TMM.2011.2129498","volume":"13","author":"H-YY Lo","year":"2011","unstructured":"Lo H-YY, Wang J-CC, Wang H-MM, De Lin S-D (2011) Cost-sensitive multi-label learning for audio tag annotation and retrieval. IEEE Trans Multimed 13(3):518\u2013529. https:\/\/doi.org\/10.1109\/TMM.2011.2129498","journal-title":"IEEE Trans Multimed"},{"key":"8341_CR27","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.1016\/j.knosys.2019.105285","journal-title":"Knowl Based Syst"},{"issue":"3","key":"8341_CR28","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/0306-4379(81)90023-5","volume":"6","author":"Z Pawlak","year":"1981","unstructured":"Pawlak Z (1981) Information systems theoretical foundations. Inf Syst 6(3):205\u2013218. https:\/\/doi.org\/10.1016\/0306-4379(81)90023-5","journal-title":"Inf Syst"},{"key":"8341_CR29","doi-asserted-by":"crossref","unstructured":"Pellegrini T and Masquelier T, Fast threshold optimization for multi-label audio tagging using Surrogate gradient learning. arXiv Prepr. arXiv2103.00833, no. Mlc, (2021), [Online]. Available: http:\/\/arxiv.org\/abs\/2103.00833.","DOI":"10.1109\/ICASSP39728.2021.9414091"},{"issue":"3","key":"8341_CR30","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/j.ipm.2018.01.002","volume":"54","author":"RB Pereira","year":"2018","unstructured":"Pereira RB, Plastino A, Zadrozny B, Merschmann LHCC (2018) Correlation analysis of performance measures for multi-label classification. Inf Process Manag 54(3):359\u2013369. https:\/\/doi.org\/10.1016\/j.ipm.2018.01.002","journal-title":"Inf Process Manag"},{"key":"8341_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106167","volume":"90","author":"W Qian","year":"2020","unstructured":"Qian W, Long X, Wang Y, Xie Y (2020a) Multi-label feature selection based on label distribution and feature complementarity. Appl Soft Comput 90:106167","journal-title":"Appl Soft Comput"},{"key":"8341_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105684","volume":"195","author":"W Qian","year":"2020","unstructured":"Qian W, Huang J, Wang Y, Shu W (2020b) Mutual information-based label distribution feature selection for multi-label learning. Knowl Based Syst 195:105684","journal-title":"Knowl Based Syst"},{"key":"8341_CR33","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.ijar.2020.10.002","volume":"128","author":"W Qian","year":"2021","unstructured":"Qian W, Huang J, Wang Y, Xie Y (2021a) Label distribution feature selection for multi-label classification with rough set. Int J Approx Reason 128:32\u201355. https:\/\/doi.org\/10.1016\/j.ijar.2020.10.002","journal-title":"Int J Approx Reason"},{"key":"8341_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106995","volume":"102","author":"W Qian","year":"2021","unstructured":"Qian W, Xiong C, Wang Y (2021b) A ranking-based feature selection for multi-label classification with fuzzy relative discernibility. Appl Soft Comput 102:106995","journal-title":"Appl Soft Comput"},{"key":"8341_CR35","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/HIS.2010.5600014","volume":"2010","author":"AM Santos","year":"2010","unstructured":"Santos AM, Canuto AMP, Neto AF (2010) Evaluating classification methods applied to multi-label tasks in different domains. 2010 10th Int Conf Hybrid Intell Syst HIS 2010:61\u201366. https:\/\/doi.org\/10.1109\/HIS.2010.5600014","journal-title":"2010 10th Int Conf Hybrid Intell Syst HIS"},{"issue":"3","key":"8341_CR36","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1198\/tech.2004.s209","volume":"46","author":"DJ Sheskin","year":"2004","unstructured":"Sheskin DJ, Crc H (2004) Handbook of parametric and nonparametric statistical procedures. Technometrics 46(3):369\u2013370. https:\/\/doi.org\/10.1198\/tech.2004.s209","journal-title":"Technometrics"},{"key":"8341_CR37","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.neucom.2015.07.118","volume":"180","author":"N Spola\u00f4r","year":"2016","unstructured":"Spola\u00f4r N, Monard MC, Tsoumakas G, Lee HD (2016) A systematic review of multi-label feature selection and a new method based on label construction. Neurocomputing 180:3\u201315. https:\/\/doi.org\/10.1016\/j.neucom.2015.07.118","journal-title":"Neurocomputing"},{"key":"8341_CR38","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 T, Ding W, Qian Y, Xu J (2020) Multilabel feature selection using ML-ReliefF and neighborhood mutual information for multilabel neighborhood decision systems. Inf Sci (ny) 537:401\u2013424","journal-title":"Inf Sci (ny)"},{"issue":"3","key":"8341_CR39","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1007\/s10994-019-05837-8","volume":"109","author":"Z-HH Tan","year":"2020","unstructured":"Tan Z-HH, Tan P, Jiang Y, Zhou Z-HH (2020) Multi-label optimal margin distribution machine. Mach Learn 109(3):623\u2013642. https:\/\/doi.org\/10.1007\/s10994-019-05837-8","journal-title":"Mach Learn"},{"key":"8341_CR40","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1007\/978-0-387-09823-4_34","volume-title":"\"Mining multi-label data,\" in Data mining and knowledge discovery handbook, no","author":"G Tsoumakas","year":"2009","unstructured":"Tsoumakas G, Katakis I, Vlahavas I (2009a) \u201cMining multi-label data,\u201d in Data mining and knowledge discovery handbook, no. Springer, Mlc, pp 667\u2013685"},{"key":"8341_CR41","first-page":"2411","volume":"12","author":"G Tsoumakas","year":"2011","unstructured":"Tsoumakas G, Spyromitros-Xioufis E, Vilcek J, Vlahavas I (2011) MULAN: A Java library for multi-label learning. J Mach Learn Res 12:2411\u20132414","journal-title":"J Mach Learn Res"},{"key":"8341_CR42","unstructured":"Tsoumakas G, Dimou A, Spyromitros E, Mezaris V, Kompatsiaris I, Vlahavas I, Correlation-based pruning of stacked binary relevance models for multi-label learning,\" in Proceedings of the 1st international workshop on learning from multi-label data, 2009b, pp. 101\u2013116."},{"key":"8341_CR43","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.ins.2017.12.034","volume":"433","author":"S Vluymans","year":"2018","unstructured":"Vluymans S, Cornelis C, Herrera F, Saeys Y (2018) Multi-label classification using a fuzzy rough neighborhood consensus. Inf Sci (ny) 433:96\u2013114","journal-title":"Inf Sci (ny)"},{"key":"8341_CR10","doi-asserted-by":"publisher","unstructured":"Venkatesan R and Er MJ, (2014) Multi-label classification method based on extreme learning machines. 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014. pp. 619\u2013624, https:\/\/doi.org\/10.1109\/ICARCV.2014.7064375.","DOI":"10.1109\/ICARCV.2014.7064375"},{"key":"8341_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107583","volume":"109","author":"R Wang","year":"2021","unstructured":"Wang R, Kwong S, Wang X, Jia Y (2021) Active k-labelsets ensemble for multi-label classification. Pattern Recognit 109:107583. https:\/\/doi.org\/10.1016\/j.patcog.2020.107583","journal-title":"Pattern Recognit"},{"key":"8341_CR45","doi-asserted-by":"publisher","unstructured":"Wang Y, Dai J, Label distribution feature selection based on mutual information in fuzzy rough set theory. in 2019 International Joint Conference on Neural Networks (IJCNN), 2019, vol. 2019-July, no. July, pp. 1\u20132, doi: https:\/\/doi.org\/10.1109\/IJCNN.2019.8851998.","DOI":"10.1109\/IJCNN.2019.8851998"},{"key":"8341_CR46","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neucom.2019.10.016","volume":"377","author":"W Weng","year":"2020","unstructured":"Weng W, Chen Y-N, Chen C-L, Wu S-X, Liu J-H (2020) Non-sparse label specific features selection for multi-label classification. Neurocomputing 377:85\u201394","journal-title":"Neurocomputing"},{"issue":"7","key":"8341_CR47","doi-asserted-by":"publisher","first-page":"2279","DOI":"10.1016\/j.patcog.2015.01.022","volume":"48","author":"B Wu","year":"2015","unstructured":"Wu B, Lyu S, Hu B-GG, Ji Q (2015) Multi-label learning with missing labels for image annotation and facial action unit recognition. Pattern Recognit 48(7):2279\u20132289. https:\/\/doi.org\/10.1016\/j.patcog.2015.01.022","journal-title":"Pattern Recognit"},{"key":"8341_CR48","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.neunet.2019.10.002","volume":"122","author":"G Wu","year":"2020","unstructured":"Wu G, Zheng R, Tian Y, Liu D (2020) Joint Ranking SVM and Binary Relevance with robust Low-rank learning for multi-label classification. Neural Netw 122:24\u201339. https:\/\/doi.org\/10.1016\/j.neunet.2019.10.002","journal-title":"Neural Netw"},{"key":"8341_CR49","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.knosys.2016.04.012","volume":"104","author":"S Xu","year":"2016","unstructured":"Xu S, Yang X, Yu H, Yu D-JJ, Yang J, Tsang ECCC (2016) Multi-label learning with label-specific feature reduction. Knowl Based Syst 104:52\u201361. https:\/\/doi.org\/10.1016\/j.knosys.2016.04.012","journal-title":"Knowl Based Syst"},{"issue":"4","key":"8341_CR50","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1109\/TEVC.2015.2504420","volume":"20","author":"B Xue","year":"2015","unstructured":"Xue B, Zhang M, Browne WN, Yao X (2015) A survey on evolutionary computation approaches to feature selection. IEEE Trans Evol Comput 20(4):606\u2013626","journal-title":"IEEE Trans Evol Comput"},{"issue":"1","key":"8341_CR51","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1109\/TPAMI.2014.2339815","volume":"37","author":"M Zhang","year":"2014","unstructured":"Zhang M, Wu L (2014) Lift: Multi-label learning with label-specific features. IEEE Trans Pattern Anal Mach Intell 37(1):107\u2013120","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"7","key":"8341_CR52","doi-asserted-by":"publisher","first-page":"2038","DOI":"10.1016\/j.patcog.2006.12.019","volume":"40","author":"ML Zhang","year":"2007","unstructured":"Zhang ML, Zhou ZH (2007) ML-KNN: A lazy learning approach to multi-label learning. Pattern Recognit 40(7):2038\u20132048. https:\/\/doi.org\/10.1016\/j.patcog.2006.12.019","journal-title":"Pattern Recognit"},{"issue":"8","key":"8341_CR53","doi-asserted-by":"publisher","first-page":"1819","DOI":"10.1109\/TKDE.2013.39","volume":"26","author":"M-LL Zhang","year":"2013","unstructured":"Zhang M-LL, Zhou Z-HH (2013) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819\u20131837. https:\/\/doi.org\/10.1109\/TKDE.2013.39","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"8341_CR54","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1016\/j.neucom.2014.11.062","volume":"154","author":"J-J Zhang","year":"2015","unstructured":"Zhang J-J, Fang M, Li X (2015) Multi-label learning with discriminative features for each label. Neurocomputing 154:305\u2013316","journal-title":"Neurocomputing"},{"key":"8341_CR55","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1016\/j.ijar.2018.10.009","volume":"103","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, Miao D, Zhang Z, Xu J, Luo S (2018) A three-way selective ensemble model for multi-label classification. Int J Approx Reason 103:394\u2013413. https:\/\/doi.org\/10.1016\/j.ijar.2018.10.009","journal-title":"Int J Approx Reason"},{"key":"8341_CR56","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.patcog.2019.06.003","volume":"95","author":"J Zhang","year":"2019","unstructured":"Zhang J, Luo Z, Li C, Zhou C, Li S (2019) Manifold regularized discriminative feature selection for multi-label learning. Pattern Recognit 95:136\u2013150. https:\/\/doi.org\/10.1016\/j.patcog.2019.06.003","journal-title":"Pattern Recognit"},{"key":"8341_CR57","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.ijar.2020.01.001","volume":"119","author":"S-PP Zhang","year":"2020","unstructured":"Zhang S-PP, Sun P, Mi J-SS, Feng T (2020) Belief function of Pythagorean fuzzy rough approximation space and its applications. Int J Approx Reason 119:58\u201380. https:\/\/doi.org\/10.1016\/j.ijar.2020.01.001","journal-title":"Int J Approx Reason"},{"key":"8341_CR58","doi-asserted-by":"publisher","unstructured":"Zhang ML, Zhou ZH, Zhang ML, and Zhou ZH, A k-nearest neighbor based algorithm for multi-label classification. in 2005 IEEE international conference on granular computing, 2005, vol. 2, no. December, pp. 718\u2013721, https:\/\/doi.org\/10.1109\/grc.2005.1547385.","DOI":"10.1109\/grc.2005.1547385"},{"key":"8341_CR59","doi-asserted-by":"crossref","unstructured":"Zhang L, Hu Q, Zhou Y, and Wang X, Multi-label attribute evaluation based on fuzzy rough sets. in International Conference on Rough Sets and Current Trends in Computing, 2014, pp. 100\u2013108.","DOI":"10.1007\/978-3-319-08644-6_10"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-023-08341-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-023-08341-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-023-08341-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T03:03:53Z","timestamp":1705028633000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-023-08341-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,8]]},"references-count":59,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["8341"],"URL":"https:\/\/doi.org\/10.1007\/s00500-023-08341-3","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-682249\/v1","asserted-by":"object"}]},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"type":"print","value":"1432-7643"},{"type":"electronic","value":"1433-7479"}],"subject":[],"published":{"date-parts":[[2023,5,8]]},"assertion":[{"value":"27 April 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2023","order":2,"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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}