{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T07:51:12Z","timestamp":1772524272271,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T00:00:00Z","timestamp":1657238400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T00:00:00Z","timestamp":1657238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"open research project of the hubei key laboratory of intelligent geo-information processing","award":["KLIGIP-2019A03"],"award-info":[{"award-number":["KLIGIP-2019A03"]}]},{"name":"Science and Technology Project of Hubei Province-Unveiling System","award":["2021BEC007"],"award-info":[{"award-number":["2021BEC007"]}]},{"name":"Industry-University-Research Innovation Funds for Chinese Universities","award":["2020ITA05008"],"award-info":[{"award-number":["2020ITA05008"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s10115-022-01701-9","type":"journal-article","created":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T07:03:03Z","timestamp":1657263783000},"page":"2123-2140","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Learning from crowds with decision trees"],"prefix":"10.1007","volume":"64","author":[{"given":"Wenjun","family":"Yang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0620-6344","authenticated-orcid":false,"given":"Chaoqun","family":"Li","sequence":"additional","affiliation":[]},{"given":"Liangxiao","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,8]]},"reference":[{"issue":"2\u20133","key":"1701_CR1","first-page":"255","volume":"17","author":"J Alcal\u00e1-Fdez","year":"2011","unstructured":"Alcal\u00e1-Fdez J, Fern\u00e1ndez A, Luengo J, Derrac J, Garc\u00eda S (2011) KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J Multiple-Valued Log Soft Comput 17(2\u20133):255\u2013287","journal-title":"J Multiple-Valued Log Soft Comput"},{"issue":"1","key":"1701_CR2","doi-asserted-by":"publisher","first-page":"20","DOI":"10.2307\/2346806","volume":"28","author":"AP Dawid","year":"1979","unstructured":"Dawid AP, Skene AM (1979) Maximum likelihood estimation of observer error-rates using the EM algorithm. Appl Stat 28(1):20\u201328","journal-title":"Appl Stat"},{"key":"1701_CR3","doi-asserted-by":"crossref","unstructured":"Demartini Gianluca, Difallah Djellel Eddine, Cudr\u00e9-Mauroux Philippe (2012) Zencrowd: leveraging probabilistic reasoning and crowdsourcing techniques for large-scale entity linking. In: Proceedings of the 21st world wide web conference 2012, WWW 2012, Lyon, France, pp 469\u2013478. ACM","DOI":"10.1145\/2187836.2187900"},{"key":"1701_CR4","first-page":"1","volume":"7","author":"J Demsar","year":"2006","unstructured":"Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330","journal-title":"J Mach Learn Res"},{"key":"1701_CR5","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.ins.2021.11.021","volume":"583","author":"Yu Dong","year":"2022","unstructured":"Dong Yu, Jiang L, Li C (2022) Improving data and model quality in crowdsourcing using co-training-based noise correction. Inf Sci 583:174\u2013188","journal-title":"Inf Sci"},{"key":"1701_CR6","first-page":"2677","volume":"9","author":"S Garcia","year":"2008","unstructured":"Garcia S, Herrera F (2008) An extension on statistical comparisons of classifiers over multiple data sets for all pairwise comparisons. J Mach Learn Res 9:2677\u20132694","journal-title":"J Mach Learn Res"},{"issue":"7","key":"1701_CR7","doi-asserted-by":"publisher","first-page":"1734","DOI":"10.1109\/TKDE.2016.2545658","volume":"28","author":"X Geng","year":"2016","unstructured":"Geng X (2016) Label distribution learning. IEEE Trans Knowl Data Eng 28(7):1734\u20131748","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"1701_CR8","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.knosys.2006.11.008","volume":"20","author":"MA Hall","year":"2007","unstructured":"Hall MA (2007) A decision tree-based attribute weighting filter for Naive Bayes. Knowl Based Syst 20(2):120\u2013126","journal-title":"Knowl Based Syst"},{"key":"1701_CR9","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3082496","author":"L Jiang","year":"2021","unstructured":"Jiang L, Zhang H, Tao F, Li C (2021) Learning from crowds with multiple noisy label distribution propagation. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2021.3082496","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1701_CR10","doi-asserted-by":"crossref","unstructured":"Jiang L, Zhang L, Li C, Wu J (2019) A correlation-based feature weighting filter for naive bayes. IEEE Trans Knowl Data Eng 31(2):201\u2013213","DOI":"10.1109\/TKDE.2018.2836440"},{"key":"1701_CR11","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.patcog.2018.11.032","volume":"88","author":"L Jiang","year":"2019","unstructured":"Jiang L, Zhang L, Liangjun Yu, Wang D (2019) Class-specific attribute weighted naive bayes. Pattern Recogn 88:321\u2013330","journal-title":"Pattern Recogn"},{"key":"1701_CR12","doi-asserted-by":"crossref","unstructured":"Kamar E, Kapoor A, Horvitz E (2015) Identifying and accounting for task-dependent bias in crowdsourcing. In: Proceedings of the third AAAI conference on human computation and crowdsourcing, HCOMP 2015, San Diego, California, USA, pp 92\u2013101. AAAI Press","DOI":"10.1609\/hcomp.v3i1.13238"},{"key":"1701_CR13","unstructured":"Karger DR, Oh S, Shah D (2011) Iterative learning for reliable crowdsourcing systems. In: Advances in neural information processing systems 24: 25th annual conference on neural information processing systems 2011. Proceedings of a meeting held 12\u201314, Granada, Spain, pp 1953\u20131961. ACM"},{"issue":"3","key":"1701_CR14","doi-asserted-by":"publisher","first-page":"794","DOI":"10.1109\/TKDE.2014.2327026","volume":"27","author":"A Kurve","year":"2015","unstructured":"Kurve A, Miller DJ, Kesidis G (2015) Multicategory crowdsourcing accounting for variable task difficulty, worker skill, and worker intention. IEEE Trans Knowl Data Eng 27(3):794\u2013809","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1701_CR15","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.engappai.2019.04.004","volume":"82","author":"C Li","year":"2019","unstructured":"Li C, Jiang L, Wenqiang X (2019) Noise correction to improve data and model quality for crowdsourcing. Eng Appl Artif Intell 82:184\u2013191","journal-title":"Eng Appl Artif Intell"},{"key":"1701_CR16","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.knosys.2016.06.003","volume":"107","author":"C Li","year":"2016","unstructured":"Li C, Sheng VS, Jiang L, Li H (2016) Noise filtering to improve data and model quality for crowdsourcing. Knowl Based Syst 107:96\u2013103","journal-title":"Knowl Based Syst"},{"key":"1701_CR17","unstructured":"Ma Y Olshevsky A, Szepesv\u00e1ri C, Saligrama V (2018) Gradient descent for sparse rank-one matrix completion for crowd-sourced aggregation of sparsely interacting workers. In: Proceedings of the 35th international conference on machine learning, ICML 2018, Stockholmsm\u00e4ssan, Stockholm, Sweden, 2018, volume\u00a080 of proceedings of machine learning research, pp 3341\u20133350. PMLR"},{"key":"1701_CR18","first-page":"1297","volume":"11","author":"VC Raykar","year":"2010","unstructured":"Raykar VC, Yu S, Zhao LH, Valadez GH, Florin C, Bogoni L, Moy L (2010) Learning from crowds. J Mach Learn Res 11:1297\u20131322","journal-title":"J Mach Learn Res"},{"key":"1701_CR19","doi-asserted-by":"crossref","unstructured":"Rodrigues F, Pereira FC (2018) Deep learning from crowds. In: McIlraith SA, Weinberger KQ (eds) Proceedings of the 32th AAAI conference on artificial intelligence, pp 1611\u20131618","DOI":"10.1609\/aaai.v32i1.11506"},{"key":"1701_CR20","unstructured":"Rodrigues F, Pereira FC, Ribeiro B (2014) Gaussian process classification and active learning with multiple annotators. In: Proceedings of the 31th international conference on machine learning, ICML 2014, Beijing, China, 2014, volume\u00a032 of JMLR workshop and conference proceedings, pp 433\u2013441. JMLR.org"},{"key":"1701_CR21","doi-asserted-by":"crossref","unstructured":"Sheng VS, Provost FJ, Ipeirotis PG (2008) Get another label? improving data quality and data mining using multiple, noisy labelers. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, Las Vegas, Nevada, USA, pp 614\u2013622. ACM","DOI":"10.1145\/1401890.1401965"},{"key":"1701_CR22","doi-asserted-by":"crossref","unstructured":"Sheng VS, Zhang J (2019) Machine learning with crowdsourcing: a brief summary of the past research and future directions. In: The Thirty-third AAAI conference on artificial intelligence, AAAI 2019, Honolulu, Hawaii, USA, 2019, pp 9837\u20139843. AAAI Press","DOI":"10.1609\/aaai.v33i01.33019837"},{"issue":"7","key":"1701_CR23","doi-asserted-by":"publisher","first-page":"2521","DOI":"10.1007\/s10115-020-01475-y","volume":"62","author":"F Tao","year":"2020","unstructured":"Tao F, Jiang L, Li C (2020) Label similarity-based weighted soft majority voting and pairing for crowdsourcing. Knowl Inf Syst 62(7):2521\u20132538","journal-title":"Knowl Inf Syst"},{"key":"1701_CR24","doi-asserted-by":"publisher","first-page":"104474","DOI":"10.1016\/j.engappai.2021.104474","volume":"106","author":"F Tao","year":"2021","unstructured":"Tao F, Jiang L, Li C (2021) Differential evolution-based weighted soft majority voting for crowdsourcing. Eng Appl Artif Intell 106:104474","journal-title":"Eng Appl Artif Intell"},{"issue":"1","key":"1701_CR25","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1109\/TKDE.2007.190672","volume":"20","author":"F Wang","year":"2008","unstructured":"Wang F, Zhang C (2008) Label propagation through linear neighborhoods. IEEE Trans Knowl Data Eng 20(1):55\u201367","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1701_CR26","unstructured":"Welinder P, Branson S, Belongie SJ, Perona P (2010) The multidimensional wisdom of crowds. In: Advances in neural information processing systems 23: 24th Annual conference on neural information processing systems 2010. Proceedings of a meeting held 6\u20139 December 2010, Vancouver, British Columbia, Canada, pp 2424\u20132432. Curran Associates, Inc"},{"key":"1701_CR27","volume-title":"Data mining: practical machine learning tools and techniques","author":"IH Witten","year":"2011","unstructured":"Witten IH, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann, Elsevier","edition":"3"},{"key":"1701_CR28","doi-asserted-by":"crossref","unstructured":"Wu M, Li Q, Zhang J, Cui S, Li D, Qi Y (2017) A robust inference algorithm for crowd sourced categorization. In: 12th international conference on intelligent systems and knowledge engineering, ISKE 2017, Nanjing, China, 2017, pp 1\u20136. IEEE","DOI":"10.1109\/ISKE.2017.8258809"},{"key":"1701_CR29","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1016\/j.ins.2020.08.117","volume":"546","author":"X Wenqiang","year":"2021","unstructured":"Wenqiang X, Jiang L, Li C (2021) Improving data and model quality in crowdsourcing using cross-entropy-based noise correction. Inf Sci 546:803\u2013814","journal-title":"Inf Sci"},{"key":"1701_CR30","doi-asserted-by":"crossref","unstructured":"Zhang H, Jiang L, Xu W (2019) Multiple noisy label distribution propagation for crowdsourcing. In: Proceedings of the twenty-eighth international joint conference on artificial intelligence, IJCAI 2019, Macao, China, 2019, pp 1473\u20131479. Morgan Kaufmann","DOI":"10.24963\/ijcai.2019\/204"},{"key":"1701_CR31","first-page":"2853","volume":"16","author":"J Zhang","year":"2015","unstructured":"Zhang J, Sheng VS, Nicholson B, Xindong W (2015) CEKA: a tool for mining the wisdom of crowds. J Mach Learn Res 16:2853\u20132858","journal-title":"J Mach Learn Res"},{"issue":"4","key":"1701_CR32","doi-asserted-by":"publisher","first-page":"1080","DOI":"10.1109\/TKDE.2015.2504974","volume":"28","author":"J Zhang","year":"2016","unstructured":"Zhang J, Sheng VS, Jian W, Xindong W (2016) Multi-class ground truth inference in crowdsourcing with clustering. IEEE Trans Knowl Data Eng 28(4):1080\u20131085","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"8","key":"1701_CR33","doi-asserted-by":"publisher","first-page":"1506","DOI":"10.1109\/TKDE.2018.2860992","volume":"31","author":"J Zhang","year":"2019","unstructured":"Zhang J, Ming W, Sheng VS (2019) Ensemble learning from crowds. IEEE Trans Knowl Data Eng 31(8):1506\u20131519","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"1701_CR34","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1109\/TKDE.2014.2327039","volume":"27","author":"J Zhang","year":"2015","unstructured":"Zhang J, Xindong W, Sheng VS (2015) Imbalanced multiple noisy labeling. IEEE Trans Knowl Data Eng 27(2):489\u2013503","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1701_CR35","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.knosys.2016.02.017","volume":"100","author":"L Zhang","year":"2016","unstructured":"Zhang L, Jiang L, Li C, Kong G (2016) Two feature weighting approaches for naive bayes text classifiers. Knowl Based Syst 100:137\u2013144","journal-title":"Knowl Based Syst"},{"issue":"12","key":"1701_CR36","doi-asserted-by":"publisher","first-page":"2643","DOI":"10.1109\/TKDE.2017.2738643","volume":"29","author":"J Zhong","year":"2017","unstructured":"Zhong J, Yang P, Tang K (2017) A quality-sensitive method for learning from crowds. IEEE Trans Knowl Data Eng 29(12):2643\u20132654","journal-title":"IEEE Trans Knowl Data Eng"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-022-01701-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-022-01701-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-022-01701-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,11]],"date-time":"2023-02-11T04:24:47Z","timestamp":1676089487000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-022-01701-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,8]]},"references-count":36,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["1701"],"URL":"https:\/\/doi.org\/10.1007\/s10115-022-01701-9","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,8]]},"assertion":[{"value":"31 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 June 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 June 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}