{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:45:11Z","timestamp":1765547111864,"version":"3.37.3"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T00:00:00Z","timestamp":1695081600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T00:00:00Z","timestamp":1695081600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20191457"],"award-info":[{"award-number":["BK20191457"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Postgraduate Research & Practice Innovation Program of Jiangsu Province","award":["SJCX22_1901"],"award-info":[{"award-number":["SJCX22_1901"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176107"],"award-info":[{"award-number":["62176107"]}],"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":[[2023,11]]},"DOI":"10.1007\/s10489-023-05008-2","type":"journal-article","created":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T06:02:13Z","timestamp":1695103333000},"page":"27844-27864","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["PLVI-CE: a multi-label active learning algorithm with simultaneously considering uncertainty and diversity"],"prefix":"10.1007","volume":"53","author":[{"given":"Yan","family":"Gu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jicong","family":"Duan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9621-4158","authenticated-orcid":false,"given":"Hualong","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xibei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shang","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,19]]},"reference":[{"issue":"9","key":"5008_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3472291","volume":"54","author":"P Ren","year":"2021","unstructured":"Ren P, Xiao Y, Chang X, Huang P-Y, Li Z, Gupta BB, Chen X, Wang X (2021) A survey of deep active learning. ACM Comput Surv (CSUR) 54(9):1\u201340","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"4","key":"5008_CR2","doi-asserted-by":"publisher","first-page":"1088","DOI":"10.1109\/TNNLS.2018.2855446","volume":"30","author":"H Yu","year":"2018","unstructured":"Yu H, Yang X, Zheng S, Sun C (2018) Active learning from imbalanced data: a solution of online weighted extreme learning machine. IEEE Trans Neural Netw Learn Syst 30(4):1088\u20131103","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"5008_CR3","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1016\/j.neucom.2021.08.063","volume":"463","author":"X Gui","year":"2021","unstructured":"Gui X, Lu X, Yu G (2021) Cost-effective batch-mode multi-label active learning. Neurocomputing 463:355\u2013367","journal-title":"Neurocomputing"},{"issue":"8","key":"5008_CR4","doi-asserted-by":"publisher","first-page":"1747","DOI":"10.1109\/TNNLS.2014.2356470","volume":"26","author":"S Chakraborty","year":"2014","unstructured":"Chakraborty S, Balasubramanian V, Panchanathan S (2014) Adaptive batch mode active learning. IEEE Trans Neural Netw Learn Syst 26(8):1747\u20131760","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"5008_CR5","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.neucom.2015.04.019","volume":"166","author":"H Yu","year":"2015","unstructured":"Yu H, Sun C, Yang W, Yang X, Zuo X (2015) AL-ELM: one uncertainty-based active learning algorithm using extreme learning machine. Neurocomput 166:140\u2013150","journal-title":"Neurocomput"},{"issue":"5","key":"5008_CR6","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.1007\/s13042-020-01099-2","volume":"11","author":"F Min","year":"2020","unstructured":"Min F, Zhang S-M, Ciucci D, Wang M (2020) Three-way active learning through clustering selection. Int J Mach Learn Cybern 11(5):1033\u20131046","journal-title":"Int J Mach Learn Cybern"},{"key":"5008_CR7","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1016\/j.neucom.2015.08.037","volume":"173","author":"Y Zhang","year":"2016","unstructured":"Zhang Y, Er MJ (2016) Sequential active learning using meta-cognitive extreme learning machine. Neurocomputing 173:835\u2013844","journal-title":"Neurocomputing"},{"issue":"5","key":"5008_CR8","doi-asserted-by":"publisher","first-page":"893","DOI":"10.1136\/amiajnl-2013-002516","volume":"21","author":"DH Nguyen","year":"2014","unstructured":"Nguyen DH, Patrick JD (2014) Supervised machine learning and active learning in classification of radiology reports. J Am Med Inform Assoc 21(5):893\u2013901","journal-title":"J Am Med Inform Assoc"},{"issue":"5","key":"5008_CR9","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1007\/s11704-016-5421-x","volume":"10","author":"N Gao","year":"2016","unstructured":"Gao N, Huang S-J, Chen S (2016) Multi-label active learning by model guided distribution matching. Front Comput Sci 10(5):845\u2013855","journal-title":"Front Comput Sci"},{"issue":"6","key":"5008_CR10","doi-asserted-by":"publisher","first-page":"2712","DOI":"10.1109\/TIP.2016.2549459","volume":"25","author":"X-Y Jing","year":"2016","unstructured":"Jing X-Y, Wu F, Li Z, Hu R, Zhang D (2016) Multi-label dictionary learning for image annotation. IEEE Trans Image Process 25(6):2712\u20132725","journal-title":"IEEE Trans Image Process"},{"key":"5008_CR11","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/j.patcog.2018.01.022","volume":"78","author":"Y Liu","year":"2018","unstructured":"Liu Y, Wen K, Gao Q, Gao X, Nie F (2018) SVM based multi-label learning with missing labels for image annotation. Pattern Recognit 78:307\u2013317","journal-title":"Pattern Recognit"},{"issue":"4","key":"5008_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3161606","volume":"9","author":"O Reyes","year":"2018","unstructured":"Reyes O, Ventura S (2018) Evolutionary strategy to perform batch-mode active learning on multi-label data. ACM Trans Int Syst Technol (TIST) 9(4):1\u201326","journal-title":"ACM Trans Int Syst Technol (TIST)"},{"key":"5008_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109226","volume":"251","author":"X-Y Min","year":"2022","unstructured":"Min X-Y, Qian K, Zhang B-W, Song G, Min F (2022) Multi-label active learning through serial-parallel neural networks. Knowl-Based Syst 251:109226","journal-title":"Knowl-Based Syst"},{"key":"5008_CR14","doi-asserted-by":"crossref","unstructured":"Ye C, Wu J, Sheng VS, Zhao P, Cui Z (2015) Multi-label active learning with label correlation for image classification. In: 2015 IEEE international conference on image processing (ICIP). IEEE, pp 3437\u20133441","DOI":"10.1109\/ICIP.2015.7351442"},{"key":"5008_CR15","doi-asserted-by":"crossref","unstructured":"Wu J, Zhao S, Sheng VS, Zhao P, Cui Z (2016) Multi-label active learning for image classification with asymmetrical conditional dependence. In: 2016 IEEE international conference on multimedia and expo (ICME). IEEE, pp 1\u20136","DOI":"10.1109\/ICME.2016.7552899"},{"issue":"6","key":"5008_CR16","doi-asserted-by":"publisher","first-page":"1156","DOI":"10.1109\/TMM.2017.2652065","volume":"19","author":"J Wu","year":"2017","unstructured":"Wu J, Zhao S, Sheng VS, Zhang J, Ye C, Zhao P, Cui Z (2017) Weak-labeled active learning with conditional label dependence for multilabel image classification. IEEE Trans Multimed 19(6):1156\u20131169","journal-title":"IEEE Trans Multimed"},{"key":"5008_CR17","doi-asserted-by":"crossref","unstructured":"Yang B, Sun J-T, Wang T, Chen Z (2009) Effective multi-label active learning for text classification. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, pp 917\u2013926","DOI":"10.1145\/1557019.1557119"},{"issue":"1","key":"5008_CR18","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1109\/TAFFC.2020.3038401","volume":"14","author":"X Kang","year":"2020","unstructured":"Kang X, Shi X, Wu Y, Ren F (2020) Active learning with complementary sampling for instructing class-biased multi-label text emotion classification. IEEE Trans Affect Comput 14(1):523\u2013536","journal-title":"IEEE Trans Affect Comput"},{"key":"5008_CR19","doi-asserted-by":"crossref","unstructured":"Shi W, Liu X, Yu Q (2017) Correlation-aware multi-label active learning for web service tag recommendation. In: 2017 IEEE international conference on web services (ICWS). IEEE, pp 229\u2013236","DOI":"10.1109\/ICWS.2017.37"},{"key":"5008_CR20","doi-asserted-by":"crossref","unstructured":"Carrillo D, L\u00f3pez VF, Moreno MN (2013) Multi-label classification for recommender systems. Trends Pract Appl Agents Multiagent Syst 181\u2013188","DOI":"10.1007\/978-3-319-00563-8_22"},{"issue":"3","key":"5008_CR21","doi-asserted-by":"publisher","first-page":"1184","DOI":"10.3390\/s22031184","volume":"22","author":"IM El-Hasnony","year":"2022","unstructured":"El-Hasnony IM, Elzeki OM, Alshehri A, Salem H (2022) Multi-label active learning-based machine learning model for heart disease prediction. Sensors 22(3):1184","journal-title":"Sensors"},{"key":"5008_CR22","doi-asserted-by":"crossref","unstructured":"Wu J, Zhu W, Jiang Y, Sun G, Gao, Y (2018) Predicting protein functions of bacteria genomes via multi-instance multi-label active learning. In: 2018 IEEE 3rd international conference on integrated circuits and microsystems (ICICM). IEEE, pp 302\u2013307","DOI":"10.1109\/ICAM.2018.8596617"},{"key":"5008_CR23","unstructured":"Li X, Guo Y (2013) Active learning with multi-label SVM classification. In: IjCAI. Citeseer, pp 1479\u20131485"},{"key":"5008_CR24","doi-asserted-by":"crossref","unstructured":"Wu J, Sheng VS, Zhang J, Zhao P, Cui Z (2014) Multi-label active learning for image classification. In: 2014 IEEE international conference on image processing (ICIP). IEEE, pp 5227\u20135231","DOI":"10.1109\/ICIP.2014.7026058"},{"key":"5008_CR25","unstructured":"Huang S-J, Chen S, Zhou Z-H (2015) Multi-label active learning: query type matters. In: Twenty-fourth international joint conference on artificial intelligence (IjCAI). pp 946\u2013952"},{"key":"5008_CR26","doi-asserted-by":"crossref","unstructured":"Huang, S-J, Zhou, Z-H (2013) Active query driven by uncertainty and diversity for incremental multi-label learning. In: 2013 IEEE 13th international conference on data mining. IEEE, pp 1079\u20131084","DOI":"10.1109\/ICDM.2013.74"},{"key":"5008_CR27","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1016\/j.neucom.2017.08.001","volume":"273","author":"O Reyes","year":"2018","unstructured":"Reyes O, Morell C, Ventura S (2018) Effective active learning strategy for multi-label learning. Neurocomputing 273:494\u2013508","journal-title":"Neurocomputing"},{"issue":"4","key":"5008_CR28","doi-asserted-by":"publisher","first-page":"1694","DOI":"10.1109\/TIP.2017.2651372","volume":"26","author":"B Du","year":"2017","unstructured":"Du B, Wang Z, Zhang L, Zhang L, Tao D (2017) Robust and discriminative labeling for multi-label active learning based on maximum correntropy criterion. IEEE Trans on Image Process 26(4):1694\u20131707","journal-title":"IEEE Trans on Image Process"},{"issue":"5","key":"5008_CR29","doi-asserted-by":"publisher","first-page":"2091","DOI":"10.1109\/TKDE.2020.3003899","volume":"34","author":"G Yu","year":"2020","unstructured":"Yu G, Chen X, Domeniconi C, Wang J, Li Z, Zhang Z, Zhang X (2020) Cmal: cost-effective multi-label active learning by querying subexamples. IEEE Trans on Knowl Data Eng 34(5):2091\u20132105","journal-title":"IEEE Trans on Knowl Data Eng"},{"issue":"2","key":"5008_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3379504","volume":"53","author":"J Wu","year":"2020","unstructured":"Wu J, Sheng VS, Zhang J, Li H, Dadakova T, Swisher CL, Cui Z, Zhao P (2020) Multi-label active learning algorithms for image classification: overview and future promise. ACM Comput Surv (CSUR) 53(2):1\u201335","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"7","key":"5008_CR31","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1049\/iet-cvi.2016.0243","volume":"11","author":"J Wu","year":"2017","unstructured":"Wu J, Ye C, Sheng VS, Zhang J, Zhao P, Cui Z (2017) Active learning with label correlation exploration for multi-label image classification. IET Comput Vis 11(7):577\u2013584","journal-title":"IET Comput Vis"},{"issue":"2","key":"5008_CR32","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1023\/A:1007330508534","volume":"28","author":"Y Freund","year":"1997","unstructured":"Freund Y, Seung HS, Shamir E, Tishby N (1997) Selective sampling using the query by committee algorithm. Mach Learn 28(2):133\u2013168","journal-title":"Mach Learn"},{"issue":"6","key":"5008_CR33","doi-asserted-by":"publisher","first-page":"4459","DOI":"10.1109\/TCYB.2020.3027509","volume":"52","author":"M-L Zhang","year":"2020","unstructured":"Zhang M-L, Li Y-K, Yang H, Liu X-Y (2020) Towards class-imbalance aware multi-label learning. IEEE Trans Cybern 52(6):4459\u20134471","journal-title":"IEEE Trans Cybern"},{"key":"5008_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107965","volume":"118","author":"AN Tarekegn","year":"2021","unstructured":"Tarekegn AN, Giacobini M, Michalak K (2021) A review of methods for imbalanced multi-label classification. Pattern Recog 118:107965","journal-title":"Pattern Recog"},{"key":"5008_CR35","doi-asserted-by":"publisher","first-page":"28488","DOI":"10.1109\/ACCESS.2018.2839340","volume":"6","author":"H Yu","year":"2018","unstructured":"Yu H, Sun C, Yang X, Zheng S, Wang Q, Xi X (2018) Lw-elm: a fast and flexible cost-sensitive learning framework for classifying imbalanced data. IEEE Access 6:28488\u201328500","journal-title":"IEEE Access"},{"key":"5008_CR36","doi-asserted-by":"crossref","unstructured":"Suter BW (1990) The multilayer perceptron as an approximation to a bayes optimal discriminant function. IEEE Trans Neural Netw 1(4):291","DOI":"10.1109\/72.80266"},{"key":"5008_CR37","doi-asserted-by":"crossref","unstructured":"Wan EA (1990) Neural network classification: a bayesian interpretation. IEEE Trans Neural Netw 1(4):303\u2013305","DOI":"10.1109\/72.80269"},{"key":"5008_CR38","unstructured":"Huang G-B, Zhu Q-Y, Siew C-K (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE international joint conference on neural networks (IEEE Cat. No. 04CH37541). IEEE, vol 2, pp 985\u2013990"},{"issue":"2","key":"5008_CR39","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s13042-011-0019-y","volume":"2","author":"G-B Huang","year":"2011","unstructured":"Huang G-B, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybern 2(2):107\u2013122","journal-title":"Int J Mach Learn Cybern"},{"key":"5008_CR40","doi-asserted-by":"crossref","unstructured":"Huang G-B, Zhou H, Ding X, Zhang R (2011) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B (Cybernetics) 42(2):513\u2013529","DOI":"10.1109\/TSMCB.2011.2168604"},{"key":"5008_CR41","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.neucom.2012.08.010","volume":"101","author":"W Zong","year":"2013","unstructured":"Zong W, Huang G-B, Chen Y (2013) Weighted extreme learning machine for imbalance learning. Neurocomputing 101:229\u2013242","journal-title":"Neurocomputing"},{"key":"5008_CR42","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/j.ins.2022.07.182","volume":"610","author":"S Chen","year":"2022","unstructured":"Chen S, Wang R, Lu J, Wang X (2022) Stable matching-based two-way selection in multi-label active learning with imbalanced data. Inform Sci 610:281\u2013299","journal-title":"Inform Sci"},{"key":"5008_CR43","unstructured":"Kang F, Jin R, Sukthankar R (2006) Correlated label propagation with application to multi-label learning. In: 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR\u201906). IEEE, vol 2, pp 1719\u20131726"},{"key":"5008_CR44","doi-asserted-by":"crossref","unstructured":"Wang B, Tu Z, Tsotsos JK (2013) Dynamic label propagation for semi-supervised multi-class multi-label classification. In: Proceedings of the IEEE international conference on computer vision, pp 425\u2013432","DOI":"10.1109\/ICCV.2013.60"},{"key":"5008_CR45","doi-asserted-by":"crossref","unstructured":"Minaev G, Visa A, Pich\u00e9 R (2017) Comprehensive survey of similarity measures for ranked based location fingerprinting algorithm. In: 2017 international conference on indoor positioning and indoor navigation (IPIN). IEEE, pp 1\u20134","DOI":"10.1109\/IPIN.2017.8115922"},{"issue":"8","key":"5008_CR46","doi-asserted-by":"publisher","first-page":"1819","DOI":"10.1109\/TKDE.2013.39","volume":"26","author":"M-L Zhang","year":"2013","unstructured":"Zhang M-L, Zhou Z-H (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":"6","key":"5008_CR47","first-page":"411","volume":"4","author":"E Gibaja","year":"2014","unstructured":"Gibaja E, Ventura S (2014) Multi-label learning: a review of the state of the art and ongoing research. Wiley Interdiscip Rev: Data Min Knowl Discov 4(6):411\u2013444","journal-title":"Wiley Interdiscip Rev: Data Min Knowl Discov"},{"issue":"10","key":"5008_CR48","doi-asserted-by":"publisher","first-page":"11131","DOI":"10.1007\/s10489-021-03086-8","volume":"52","author":"M Wang","year":"2022","unstructured":"Wang M, Feng T, Shan Z, Min F (2022) Attribute and label distribution driven multi-label active learning. Appl Int 52(10):11131\u201311146","journal-title":"Appl Int"},{"key":"5008_CR49","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"},{"issue":"10","key":"5008_CR50","doi-asserted-by":"publisher","first-page":"2044","DOI":"10.1016\/j.ins.2009.12.010","volume":"180","author":"S Garc\u00eda","year":"2010","unstructured":"Garc\u00eda S, Fern\u00e1ndez A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inform Sci 180(10):2044\u20132064","journal-title":"Inform Sci"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-05008-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-05008-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-05008-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T23:21:58Z","timestamp":1698276118000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-05008-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,19]]},"references-count":50,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["5008"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-05008-2","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2023,9,19]]},"assertion":[{"value":"7 September 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 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 contents in this article conform to the aims and interests of Applied Intelligence Journal. We also expect to declare that the paper is our original work and it hasnt been submitted to any other journals or conferences.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"The authors declared that they have no known conflicts of interest to this work.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}