{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T06:57:24Z","timestamp":1764053844560,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819947515"},{"type":"electronic","value":"9789819947522"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-981-99-4752-2_24","type":"book-chapter","created":{"date-parts":[[2023,7,30]],"date-time":"2023-07-30T16:02:10Z","timestamp":1690732930000},"page":"285-297","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Instance Weighting-Based Noise Correction for Crowdsourcing"],"prefix":"10.1007","author":[{"given":"Qiang","family":"Ji","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liangxiao","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,31]]},"reference":[{"key":"24_CR1","unstructured":"Buecheler, T., Sieg, J.H., F\u00fcchslin, R.M., Pfeifer, R.: Crowdsourcing, open innovation and collective intelligence in the scientific method - a research agenda and operational framework. In: Proceedings of the Twelfth International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2010, Odense, Denmark, August 19\u201323, 2010, pp. 679\u2013686. MIT Press (2010)"},{"issue":"1","key":"24_CR2","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1177\/1745691610393980","volume":"6","author":"M Buhrmester","year":"2011","unstructured":"Buhrmester, M., Kwang, T., Gosling, S.D.: Amazon\u2019s mechanical turk: a new source of inexpensive, yet high-quality, data? Perspect. Psychol. Sci. 6(1), 3\u20135 (2011)","journal-title":"Perspect. Psychol. Sci."},{"key":"24_CR3","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1016\/j.ins.2022.05.066","volume":"606","author":"Z Chen","year":"2022","unstructured":"Chen, Z., Jiang, L., Li, C.: Label augmented and weighted majority voting for crowd-sourcing. Inf. Sci. 606, 397\u2013409 (2022)","journal-title":"Inf. Sci."},{"issue":"9","key":"24_CR4","doi-asserted-by":"publisher","first-page":"5752","DOI":"10.1002\/int.22812","volume":"37","author":"Z Chen","year":"2022","unstructured":"Chen, Z., Jiang, L., Li, C.: Label distribution-based noise correction for multiclass crowdsourcing. Int. J. Intell. Syst. 37(9), 5752\u20135767 (2022)","journal-title":"Int. J. Intell. Syst."},{"key":"24_CR5","first-page":"1","volume":"7","author":"J Demsar","year":"2006","unstructured":"Demsar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1\u201330 (2006)","journal-title":"J. Mach. Learn. Res."},{"key":"24_CR6","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.ins.2021.11.021","volume":"583","author":"Y Dong","year":"2022","unstructured":"Dong, Y., Jiang, L., Li, C.: Improving data and model quality in crowdsourcing using co-training-based noise correction. Inf. Sci. 583, 174\u2013188 (2022)","journal-title":"Inf. Sci."},{"key":"24_CR7","unstructured":"Gamberger, D., Lavrac, N., Groselj, C.: Experiments with noise filtering in a medical domain. In: Bratko, I., Dzeroski, S. (eds.) Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pp. 143\u2013151. Morgan Kaufmann (1999)"},{"issue":"11","key":"24_CR8","doi-asserted-by":"publisher","first-page":"6558","DOI":"10.1109\/TNNLS.2021.3082496","volume":"33","author":"L Jiang","year":"2022","unstructured":"Jiang, L., Zhang, H., Tao, F., Li, C.: Learning from crowds with multiple noisy label distribution propagation. IEEE Trans. Neural Networks Learn. Syst. 33(11), 6558\u20136568 (2022)","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"issue":"2","key":"24_CR9","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1109\/TKDE.2018.2836440","volume":"31","author":"L Jiang","year":"2019","unstructured":"Jiang, L., Zhang, L., Li, C., Wu, J.: A correlation-based feature weighting filter for naive bayes. IEEE Trans. Knowl. Data Eng. 31(2), 201\u2013213 (2019)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"24_CR10","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., Xu, W.: Noise correction to improve data and model quality for crowdsourcing. Eng. Appl. Artif. Intell. 82, 184\u2013191 (2019)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"24_CR11","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, V.S., Jiang, L., Li, H.: Noise filtering to improve data and model quality for crowdsourcing. Knowl. Based Syst. 107, 96\u2013103 (2016)","journal-title":"Knowl. Based Syst."},{"key":"24_CR12","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1016\/j.inffus.2022.11.002","volume":"91","author":"X Li","year":"2023","unstructured":"Li, X., Li, C., Jiang, L.: A multi-view-based noise correction algorithm for crowd-sourcing learning. Information Fusion 91, 529\u2013541 (2023)","journal-title":"Information Fusion"},{"key":"24_CR13","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.eswa.2016.09.003","volume":"66","author":"B Nicholson","year":"2016","unstructured":"Nicholson, B., Sheng, V.S., Zhang, J.: Label noise correction and application in crowdsourcing. Expert Syst. Appl. 66, 149\u2013162 (2016)","journal-title":"Expert Syst. Appl."},{"key":"24_CR14","unstructured":"Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann (1993)"},{"issue":"12","key":"24_CR15","doi-asserted-by":"publisher","first-page":"2409","DOI":"10.1109\/TPAMI.2017.2648786","volume":"39","author":"F Rodrigues","year":"2017","unstructured":"Rodrigues, F., Louren\u00e7o, M., Ribeiro, B., Pereira, F.C.: Learning supervised topic models for classification and regression from crowds. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2409\u20132422 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"12","key":"24_CR16","doi-asserted-by":"publisher","first-page":"1428","DOI":"10.1016\/j.patrec.2013.05.012","volume":"34","author":"F Rodrigues","year":"2013","unstructured":"Rodrigues, F., Pereira, F.C., Ribeiro, B.: Learning from multiple annotators: distinguishing good from random labelers. Pattern Recognit. Lett. 34(12), 1428\u20131436 (2013)","journal-title":"Pattern Recognit. Lett."},{"key":"24_CR17","doi-asserted-by":"crossref","unstructured":"Sheng, V.S., Provost, F.J., Ipeirotis, P.G.: Get another label? improving data quality and data mining using multiple, noisy labelers. In: Li, Y., Liu, B., Sarawagi, S. (eds.) Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, Nevada, USA, August 24\u201327, 2008, pp. 614\u2013622. ACM (2008)","DOI":"10.1145\/1401890.1401965"},{"issue":"1","key":"24_CR18","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1109\/TNNLS.2018.2836969","volume":"30","author":"D Tao","year":"2019","unstructured":"Tao, D., Cheng, J., Yu, Z., Yue, K., Wang, L.: Domain-weighted majority voting for crowdsourcing. IEEE Trans. Neural Networks Learn. Syst. 30(1), 163\u2013174 (2019)","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"24_CR19","volume-title":"Data mining: practical machine learning tools and techniques","author":"IH Witten","year":"2011","unstructured":"Witten, I.H., Frank, E., Hall, M.A.: Data mining: practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann, Elsevier (2011)","edition":"3"},{"key":"24_CR20","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1016\/j.ins.2020.08.117","volume":"546","author":"W Xu","year":"2021","unstructured":"Xu, W., Jiang, L., Li, C.: Improving data and model quality in crowdsourcing using cross-entropy-based noise correction. Inf. Sci. 546, 803\u2013814 (2021)","journal-title":"Inf. Sci."},{"issue":"3","key":"24_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11432-020-3067-8","volume":"66","author":"W Yang","year":"2023","unstructured":"Yang, W., Li, C., Jiang, L.: Learning from crowds with robust support vector machines. Science China Inf. Sci. 66(3), 1\u201317 (2023)","journal-title":"Science China Inf. Sci."},{"issue":"5","key":"24_CR22","doi-asserted-by":"publisher","first-page":"1675","DOI":"10.1109\/TNNLS.2017.2677468","volume":"29","author":"J Zhang","year":"2018","unstructured":"Zhang, J., Sheng, V.S., Li, T., Wu, X.: Improving crowdsourced label quality using noise correction. IEEE Trans. Neural Networks Learn. Syst. 29(5), 1675\u20131688 (2018)","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"24_CR23","first-page":"2853","volume":"16","author":"J Zhang","year":"2015","unstructured":"Zhang, J., Sheng, V.S., Nicholson, B., Wu, X.: CEKA: a tool for mining the wisdom of crowds. J. Mach. Learn. Res. 16, 2853\u20132858 (2015)","journal-title":"J. Mach. Learn. Res."},{"issue":"4","key":"24_CR24","doi-asserted-by":"publisher","first-page":"1080","DOI":"10.1109\/TKDE.2015.2504974","volume":"28","author":"J Zhang","year":"2016","unstructured":"Zhang, J., Sheng, V.S., Wu, J., Wu, X.: Multi-class ground truth inference in crowd-sourcing with clustering. IEEE Trans. Knowl. Data Eng. 28(4), 1080\u20131085 (2016)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"2","key":"24_CR25","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1109\/TKDE.2014.2327039","volume":"27","author":"J Zhang","year":"2015","unstructured":"Zhang, J., Wu, X., Sheng, V.S.: Imbalanced multiple noisy labeling. IEEE Trans. Knowl. Data Eng. 27(2), 489\u2013503 (2015)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"5","key":"24_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-022-2225-z","volume":"17","author":"Y Zhang","year":"2023","unstructured":"Zhang, Y., Jiang, L., Li, C.: Attribute augmentation-based label integration for crowdsourcing. Front. Comp. Sci. 17(5), 175331 (2023)","journal-title":"Front. Comp. Sci."}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-4752-2_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T23:09:24Z","timestamp":1690931364000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-4752-2_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819947515","9789819947522"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-4752-2_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"31 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zhengzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2023a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2023\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}