{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T16:59:44Z","timestamp":1774285184996,"version":"3.50.1"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2023,3,30]],"date-time":"2023-03-30T00:00:00Z","timestamp":1680134400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,30]],"date-time":"2023-03-30T00:00:00Z","timestamp":1680134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2076221"],"award-info":[{"award-number":["2076221"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976194"],"award-info":[{"award-number":["61976194"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s10489-023-04562-z","type":"journal-article","created":{"date-parts":[[2023,3,30]],"date-time":"2023-03-30T12:06:20Z","timestamp":1680177980000},"page":"20110-20133","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Multi-label classification with weak labels by learning label correlation and label regularization"],"prefix":"10.1007","volume":"53","author":[{"given":"Xiaowan","family":"Ji","sequence":"first","affiliation":[]},{"given":"Anhui","family":"Tan","sequence":"additional","affiliation":[]},{"given":"Wei-Zhi","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Shenming","family":"Gu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,30]]},"reference":[{"key":"4562_CR1","doi-asserted-by":"crossref","unstructured":"Tsoumakas G, Katakis I, Vlahavas I (2009) Mining multi-label data. In: Data mining and knowledge discovery handbook. Springer, pp 667\u2013685","DOI":"10.1007\/978-0-387-09823-4_34"},{"issue":"8","key":"4562_CR2","doi-asserted-by":"publisher","first-page":"1819","DOI":"10.1109\/TKDE.2013.39","volume":"26","author":"ML Zhang","year":"2013","unstructured":"Zhang ML, Zhou ZH (2013) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819\u20131837","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"11","key":"4562_CR3","doi-asserted-by":"publisher","first-page":"2844","DOI":"10.1109\/TMM.2020.2966887","volume":"22","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Wu J, Cai Z, Philip SY (2020) Multi-view multi-label learning with sparse feature selection for image annotation. IEEE Trans Multimed 22(11):2844\u20132857","journal-title":"IEEE Trans Multimed"},{"issue":"2","key":"4562_CR4","doi-asserted-by":"publisher","first-page":"102441","DOI":"10.1016\/j.ipm.2020.102441","volume":"58","author":"R Wang","year":"2021","unstructured":"Wang R, Ridley R, Qu W, Dai X et al (2021) A novel reasoning mechanism for multi-label text classification. Inf Process Manag 58(2):102441","journal-title":"Inf Process Manag"},{"issue":"1","key":"4562_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-016-1232-1","volume":"17","author":"R Cerri","year":"2016","unstructured":"Cerri R, Barros RC, PLF de Carvalho AC, Jin Y (2016) Reduction strategies for hierarchical multi-label classification in protein function prediction. BMC Bioinf 17(1):1\u201324","journal-title":"BMC Bioinf"},{"issue":"1","key":"4562_CR6","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1109\/TPAMI.2014.2339815","volume":"37","author":"ML Zhang","year":"2014","unstructured":"Zhang ML, 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":"4562_CR7","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","journal-title":"Pattern Recognit"},{"issue":"5","key":"4562_CR8","first-page":"1191","volume":"42","author":"XW Liu","year":"2019","unstructured":"Liu XW, Zhu XZ, Li MM, Wang L, Zhu E, Liu TL, Kloft M, Shen DG, Yin JP, Gao W (2019) Multiple kernel k k-means with incomplete kernels. IEEE Trans Pattern Anal Mach Intell 42(5):1191\u20131204","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"4562_CR9","doi-asserted-by":"publisher","first-page":"876","DOI":"10.1109\/TCYB.2017.2663838","volume":"48","author":"J Huang","year":"2017","unstructured":"Huang J, Li GR, Huang QM, Wu XD (2017) Joint feature selection and classification for multilabel learning. IEEE Trans Cybern 48(3):876\u2013889","journal-title":"IEEE Trans Cybern"},{"issue":"12","key":"4562_CR10","doi-asserted-by":"publisher","first-page":"3309","DOI":"10.1109\/TKDE.2016.2608339","volume":"28","author":"J Huang","year":"2016","unstructured":"Huang J, Li GR, Huang QM, Wu XD (2016) Learning label-specific features and class-dependent labels for multi-label classification. IEEE Trans Knowl Data Eng 28(12):3309\u20133323","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"6","key":"4562_CR11","doi-asserted-by":"publisher","first-page":"1081","DOI":"10.1109\/TKDE.2017.2785795","volume":"30","author":"Y Zhu","year":"2017","unstructured":"Zhu Y, Kwok JT, Zhou ZH (2017) Multi-label learning with global and local label correlation. IEEE Trans Knowl ta Eng 30(6):1081\u20131094","journal-title":"IEEE Trans Knowl ta Eng"},{"key":"4562_CR12","doi-asserted-by":"publisher","first-page":"106709","DOI":"10.1016\/j.knosys.2020.106709","volume":"213","author":"P Teisseyre","year":"2021","unstructured":"Teisseyre P (2021) Classifier chains for positive unlabelled multi-label learning. Knowl-Based Syst 213:106709","journal-title":"Knowl-Based Syst"},{"key":"4562_CR13","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.ins.2019.04.021","volume":"492","author":"J Huang","year":"2019","unstructured":"Huang J, Qin F, Zheng X, Cheng Z, Yuan Z, Zhang W, Huang Q (2019) Improving multi-label classification with missing labels by learning label-specific features. Inform Sci 492:124\u2013146","journal-title":"Inform Sci"},{"issue":"6","key":"4562_CR14","doi-asserted-by":"publisher","first-page":"3375","DOI":"10.1007\/s10489-020-02008-4","volume":"51","author":"YY Guan","year":"2021","unstructured":"Guan YY, Li WH, Zhang BX, Han B, Ji ML (2021) Multi-label classification by formulating label-specific features from simultaneous instance level and feature level. Appl Intell 51(6):3375\u20133390","journal-title":"Appl Intell"},{"issue":"5","key":"4562_CR15","doi-asserted-by":"publisher","first-page":"1197","DOI":"10.1109\/TFUZZ.2021.3053844","volume":"30","author":"L Sun","year":"2021","unstructured":"Sun L, Yin TY, Ding WP, Qian YH, Xu JC (2021) Feature selection with missing labels using multilabel fuzzy neighborhood rough sets and maximum relevance minimum redundancy. IEEE Trans Fuzzy Syst 30(5):1197\u20131211. https:\/\/ieeexplore.ieee.org\/abstract\/document\/9333666\/","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"4562_CR16","unstructured":"Charte F, Rivera AJ, Del Jesus MJ (2016) Multilabel classification: problem analysis, metrics and techniques. Springer"},{"issue":"9","key":"4562_CR17","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","journal-title":"Pattern Recognit"},{"key":"4562_CR18","unstructured":"Elisseeff A, Weston J (2001) A kernel method for multi-labelled classification. In: International conference on neural information processing systems: natural and synthetic, pp 681\u2013687"},{"key":"4562_CR19","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.neucom.2020.06.101","volume":"413","author":"LJ Sun","year":"2020","unstructured":"Sun LJ, Ye P, Lyu GY, Feng SH, Dai GJ, Zhang H (2020) Weakly-supervised multi-label learning with noisy features and incomplete labels. Neurocomputing 413:61\u201371","journal-title":"Neurocomputing"},{"key":"4562_CR20","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.ins.2022.02.011","volume":"594","author":"AH Tan","year":"2022","unstructured":"Tan AH, Ji XW, Liang JY, Tao YZ, Wu WZ, Pedrycz W (2022) Weak multi-label learning with missing labels via instance granular discrimination. Inf Sci 594:200\u2013216. https:\/\/doi.org\/10.1016\/j.ins.2022.02.011","journal-title":"Inf Sci"},{"issue":"5","key":"4562_CR21","doi-asserted-by":"publisher","first-page":"3841","DOI":"10.1109\/TCYB.2020.3015269","volume":"52","author":"J Zhang","year":"2020","unstructured":"Zhang J, Li SZ, Jiang M, Tan KC (2020) Learning from weakly labeled data based on manifold regularized sparse model. IEEE Trans Cybern 52(5):3841\u20133854","journal-title":"IEEE Trans Cybern"},{"key":"4562_CR22","doi-asserted-by":"crossref","unstructured":"Tan AH, Liang JY, Wu WZ, Zhang J (2022) Semi-supervised partial multi-label classification via consistency learning. Pattern Recognit:108839","DOI":"10.1016\/j.patcog.2022.108839"},{"issue":"3","key":"4562_CR23","doi-asserted-by":"publisher","first-page":"1552","DOI":"10.1007\/s10489-020-01878-y","volume":"51","author":"LJ Sun","year":"2021","unstructured":"Sun LJ, Lyu GY, Feng SH, Huang XK (2021) Beyond missing: weakly-supervised multi-label learning with incomplete and noisy labels. Appl Intell 51(3):1552\u20131564","journal-title":"Appl Intell"},{"key":"4562_CR24","doi-asserted-by":"publisher","first-page":"724","DOI":"10.1016\/j.ins.2022.08.118","volume":"612","author":"L Sun","year":"2022","unstructured":"Sun L, Li MM, Ding WP, Zhang E, Mu XX, Xu JC (2022) Afnfs: adaptive fuzzy neighborhood-based feature selection with adaptive synthetic over-sampling for imbalanced data. Inform Sci 612:724\u2013744. https:\/\/doi.org\/10.1016\/j.ins.2022.08.118","journal-title":"Inform Sci"},{"issue":"5","key":"4562_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3342512","volume":"10","author":"A Braytee","year":"2019","unstructured":"Braytee A, Liu W, Anaissi A, Kennedy PJ (2019) Correlated multi-label classification with incomplete label space and class imbalance. ACM Trans Intell Syst Technol (TIST) 10(5):1\u201326","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"key":"4562_CR26","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1016\/j.ins.2022.08.089","volume":"613","author":"M Han","year":"2022","unstructured":"Han M, Zhang H (2022) Multiple kernel learning for label relation and class imbalance in multi-label learning. Inform Sci 613:344\u2013356","journal-title":"Inform Sci"},{"key":"4562_CR27","doi-asserted-by":"crossref","unstructured":"Bucak SS, Jin R, Jain AK (2011) Multi-label learning with incomplete class assignments. In: CVPR 2011. IEEE, pp 2801\u20132808","DOI":"10.1109\/CVPR.2011.5995734"},{"key":"4562_CR28","doi-asserted-by":"crossref","unstructured":"Kong XN, Wu ZM, Li LJ, Zhang RF, Yu PS, Wu H, Fan W (2014) Large-scale multi-label learning with incomplete label assignments. In: Proceedings of the 2014 SIAM international conference on data mining. SIAM, pp 920\u2013928","DOI":"10.1137\/1.9781611973440.105"},{"key":"4562_CR29","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.knosys.2018.08.018","volume":"163","author":"Z-F He","year":"2019","unstructured":"He Z-F, Yang M, Gao Y, Liu H-D, Yin Y (2019) Joint multi-label classification and label correlations with missing labels and feature selection. Knowl-Based Syst 163:145\u2013158","journal-title":"Knowl-Based Syst"},{"issue":"8","key":"4562_CR30","doi-asserted-by":"publisher","first-page":"875","DOI":"10.1007\/s11263-018-1085-3","volume":"126","author":"BY Wu","year":"2018","unstructured":"Wu BY, Jia F, Liu W, Ghanem B, Lyu SW (2018) Multi-label learning with missing labels using mixed dependency graphs. Int J Comput Vis 126(8):875\u2013896","journal-title":"Int J Comput Vis"},{"key":"4562_CR31","first-page":"2301","volume":"26","author":"M Xu","year":"2013","unstructured":"Xu M, Jin R, Zhou ZH (2013) Speedup matrix completion with side information: application to multi-label learning. Adv Neural Inf Process Syst 26:2301\u20132309","journal-title":"Adv Neural Inf Process Syst"},{"key":"4562_CR32","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.neunet.2018.01.011","volume":"101","author":"B Liu","year":"2018","unstructured":"Liu B, Li Y, Xu Z (2018) Manifold regularized matrix completion for multi-label learning with admm. Neural Netw 101:57\u201367","journal-title":"Neural Netw"},{"key":"4562_CR33","doi-asserted-by":"crossref","unstructured":"Tan QY, Yu GX, Domeniconi C, Wang J, Zhang ZL (2018) Multi-view weak-label learning based on matrix completion. In: Proceedings of the 2018 SIAM international conference on data mining, pp 450\u2013458","DOI":"10.1137\/1.9781611975321.51"},{"key":"4562_CR34","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1016\/j.ins.2020.12.010","volume":"554","author":"R Wang","year":"2021","unstructured":"Wang R, Ye S, Li K, Kwong S (2021) Bayesian network based label correlation analysis for multi-label classifier chain. Inform Sci 554:256\u2013275","journal-title":"Inform Sci"},{"key":"4562_CR35","doi-asserted-by":"crossref","unstructured":"Vasisht D, Damianou A, Varma M, Kapoor A (2014) Active learning for sparse bayesian multilabel classification. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, pp 472\u2013481","DOI":"10.1145\/2623330.2623759"},{"key":"4562_CR36","unstructured":"Li X, Zhao FP, Guo YH (2015) Conditional restricted boltzmann machines for multi-label learning with incomplete labels. In: Artificial intelligence and statistics. PMLR, pp 635\u2013643"},{"key":"4562_CR37","doi-asserted-by":"crossref","unstructured":"Xie MK, Huang SJ (2018) Partial multi-label learning. In: Proceedings of the AAAI conference on artificial intelligence, pp 6454\u20136461","DOI":"10.1609\/aaai.v32i1.11644"},{"key":"4562_CR38","doi-asserted-by":"crossref","unstructured":"Yu GX, Chen X, Domeniconi C, Wang J, Li Z, Zhang ZL, Wu XD (2018) Feature-induced partial multi-label learning. In: 2018 IEEE international conference on data mining (ICDM), pp 1398\u20131403","DOI":"10.1109\/ICDM.2018.00192"},{"issue":"7","key":"4562_CR39","doi-asserted-by":"publisher","first-page":"3676","DOI":"10.1109\/TPAMI.2021.3059290","volume":"44","author":"MK Xie","year":"2021","unstructured":"Xie MK, Huang SJ (2021) Partial multi-label learning with noisy label identification. IEEE Trans Pattern Anal Mach Intell 44(7):3676\u20133687. https:\/\/doi.org\/10.1109\/TPAMI.2021.3059290","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4562_CR40","doi-asserted-by":"crossref","unstructured":"Li ZW, Lyu GY, Feng SH (2020) Partial multi-label learning via multi-subspace representation. In: IJCAI, pp 2612\u20132618","DOI":"10.24963\/ijcai.2020\/362"},{"issue":"10","key":"4562_CR41","doi-asserted-by":"publisher","first-page":"3587","DOI":"10.1109\/TPAMI.2020.2985210","volume":"43","author":"ML Zhang","year":"2020","unstructured":"Zhang ML, Fang JP (2020) Partial multi-label learning via credible label elicitation. IEEE Trans Pattern Anal Mach Intell 43(10):3587\u20133599","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4562_CR42","doi-asserted-by":"publisher","unstructured":"Liu WW, Wang HB, Shen XB, Tsang I (2021) The emerging trends of multi-label learning. IEEE Trans Pattern Anal Mach Intell. https:\/\/doi.org\/10.1109\/TPAMI.2021.3119334","DOI":"10.1109\/TPAMI.2021.3119334"},{"key":"4562_CR43","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1016\/j.neucom.2017.07.044","volume":"273","author":"W Weng","year":"2018","unstructured":"Weng W, Lin YJ, Wu SX, Li YW, Kang Y (2018) Multi-label learning based on label-specific features and local pairwise label correlation. Neurocomputing 273:385\u2013394","journal-title":"Neurocomputing"},{"key":"4562_CR44","unstructured":"Dembczynski K, Jachnik A, Kotlowski W, Waegeman W, H\u00fcllermeier E (2013) Optimizing the f-measure in multi-label classification: plug-in rule approach versus structured loss minimization. In: International conference on machine learning. PMLR, pp 1130\u20131138"},{"key":"4562_CR45","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.035","volume":"73","author":"HX Guo","year":"2017","unstructured":"Guo HX, Li YJ, Shang J, Gu MY, Huang YY, Gong B (2017) Learning from class-imbalanced data: review of methods and applications. Expert Syst Appl 73:220\u2013239","journal-title":"Expert Syst Appl"},{"key":"4562_CR46","doi-asserted-by":"crossref","unstructured":"Chen K, Lu BL, Kwok JT (2006) Efficient classification of multi-label and imbalanced data using min-max modular classifiers. In: The 2006 IEEE international joint conference on neural network proceedings. IEEE, pp 1770\u20131775","DOI":"10.1109\/IJCNN.2006.246893"},{"key":"4562_CR47","doi-asserted-by":"crossref","unstructured":"Wu BY, Lyu SW, Ghanem B (2016) Constrained submodular minimization for missing labels and class imbalance in multi-label learning. In: Proceedings of the AAAI conference on artificial intelligence, vol 30","DOI":"10.1609\/aaai.v30i1.10186"},{"key":"4562_CR48","doi-asserted-by":"publisher","unstructured":"Zhang ML, Li YK, Yang H, Liu XY (2020) Towards class-imbalance aware multi-label learning. IEEE Trans Cybern. https:\/\/doi.org\/10.1109\/TCYB.2020.3027509","DOI":"10.1109\/TCYB.2020.3027509"},{"key":"4562_CR49","doi-asserted-by":"crossref","unstructured":"Xu LL, Wang Z, Shen ZF, Wang YB, Chen EH (2014) Learning low-rank label correlations for multi-label classification with missing labels. In: 2014 IEEE international conference on data mining. IEEE, pp 1067\u20131072","DOI":"10.1109\/ICDM.2014.125"},{"key":"4562_CR50","doi-asserted-by":"crossref","unstructured":"Wang J, Jebara T, Chang S-F (2008) Graph transduction via alternating minimization. In: Proceedings of the 25th international conference on machine learning, pp 1144\u20131151","DOI":"10.1145\/1390156.1390300"},{"issue":"1","key":"4562_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000016","volume":"3","author":"S Boyd","year":"2010","unstructured":"Boyd S, Parikh N, Chu E, Peleato B, Eckstein J (2010) Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn 3(1):1\u2013122","journal-title":"Found Trends Mach Learn"},{"key":"4562_CR52","first-page":"612","volume":"24","author":"ZC Lin","year":"2011","unstructured":"Lin ZC, Liu RS, Su ZX (2011) Linearized alternating direction method with adaptive penalty for low-rank representation. Adv Neural Inf Process Syst 24:612\u2013620","journal-title":"Adv Neural Inf Process Syst"},{"issue":"1","key":"4562_CR53","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1137\/080716542","volume":"2","author":"A Beck","year":"2009","unstructured":"Beck A, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imag Sci 2(1):183\u2013202","journal-title":"SIAM J Imag Sci"},{"issue":"21","key":"4562_CR54","doi-asserted-by":"publisher","first-page":"3445","DOI":"10.4236\/am.2014.521322","volume":"5","author":"L Wang","year":"2014","unstructured":"Wang L, Hu JF, Chen CZ (2014) On accelerated singular value thresholding algorithm for matrix completion. Appl Math 5(21):3445","journal-title":"Appl Math"},{"key":"4562_CR55","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.neucom.2014.08.091","volume":"163","author":"F Charte","year":"2015","unstructured":"Charte F, Rivera AJ, del Jesus MJ, Herrera F (2015) Addressing imbalance in multilabel classification: measures and random resampling algorithms. Neurocomputing 163:3\u201316","journal-title":"Neurocomputing"},{"issue":"2","key":"4562_CR56","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1109\/TKDE.2018.2833850","volume":"31","author":"A Hosseini Akbarnejad","year":"2019","unstructured":"Hosseini Akbarnejad A, Soleymani Baghshah M (2019) An efficient large-scale semi-supervised multi-label classifier capable of handling missing labels. IEEE Trans Knowl Data Eng 31(2):229\u2013242","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"4562_CR57","doi-asserted-by":"publisher","first-page":"107675","DOI":"10.1016\/j.patcog.2020.107675","volume":"111","author":"ZC Ma","year":"2021","unstructured":"Ma ZC, Chen SC (2021) Expand globally, shrink locally: discriminant multi-label learning with missing labels. Pattern Recognit 111:107675","journal-title":"Pattern Recognit"},{"key":"4562_CR58","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330","journal-title":"J Mach Learn Res"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04562-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-04562-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04562-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T11:35:23Z","timestamp":1694777723000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-04562-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,30]]},"references-count":58,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["4562"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-04562-z","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,30]]},"assertion":[{"value":"6 March 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 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 known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Competing of Interests"}}]}}