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Then, in the second stage, the initial training instances are selected from each cluster based on an adaptive candidate representative instances determination mechanism and a clusters-cyclic instance query mechanism. The comprehensive experiments demonstrate the effectiveness of the proposed method from the aspects of class coverage, classification performance, and impact on active learning.<\/jats:p>","DOI":"10.3233\/ida-205393","type":"journal-article","created":{"date-parts":[[2021,9,17]],"date-time":"2021-09-17T12:02:02Z","timestamp":1631880122000},"page":"1169-1185","source":"Crossref","is-referenced-by-count":8,"title":["A two-stage clustering-based cold-start method for active learning"],"prefix":"10.1177","volume":"25","author":[{"given":"Deniu","family":"He","sequence":"first","affiliation":[{"name":"Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Yu","sequence":"additional","affiliation":[{"name":"Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoyin","family":"Wang","sequence":"additional","affiliation":[{"name":"Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Li","sequence":"additional","affiliation":[{"name":"School of Metallurgy and Environment, Central South University, Changsha, Hunan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"2","key":"10.3233\/IDA-205393_ref1","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/S0031-3203(02)00060-2","article-title":"The global K-means clustering algorithm","volume":"36","author":"Likas","year":"2003","journal-title":"Pattern Recognition"},{"issue":"6191","key":"10.3233\/IDA-205393_ref2","doi-asserted-by":"crossref","first-page":"1492","DOI":"10.1126\/science.1242072","article-title":"Clustering by fast search and find of density peaks","volume":"344","author":"Rodriguez","year":"2014","journal-title":"Science"},{"key":"10.3233\/IDA-205393_ref3","first-page":"55","article-title":"Active learning literature survey","volume":"52","author":"Settles","year":"2010","journal-title":"University of Wisconsin, Madison"},{"issue":"4","key":"10.3233\/IDA-205393_ref4","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1109\/TKDE.2011.104","article-title":"Manifold adaptive experimental design for text categorization","volume":"24","author":"Cai","year":"2011","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.3233\/IDA-205393_ref5","doi-asserted-by":"crossref","unstructured":"D.D. 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Kang, K.R. Ryu and H.C. Kwon, Using cluster-based sampling to select initial training set for active learning in text classification, in: Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, 2004, pp.\u00a0384\u2013388.","DOI":"10.1007\/978-3-540-24775-3_46"},{"key":"10.3233\/IDA-205393_ref13","unstructured":"K. Konyushkova, R. Sznitman and P. Fua, Learning active learning from data, in: Proceedings of the 31th Advances in Neural Information Processing Systems, MIT Press, Cambridge, 2017, pp. 4225\u20134235."},{"key":"10.3233\/IDA-205393_ref14","doi-asserted-by":"crossref","unstructured":"K. Yu, J. Bi and V. Tresp, Active learning via transductive experimental design, in: Proceedings of the 23rd International Conference on Machine Learning, ACM, New York, 2006, pp. 1081\u20131088.","DOI":"10.1145\/1143844.1143980"},{"key":"10.3233\/IDA-205393_ref15","doi-asserted-by":"crossref","unstructured":"K. Yu, S. Zhu, W. Xu and Y. 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Huang, New balanced cctive learning model and optimization algorithm, in: Proceedings of the 27th International Joint Conference on Artificial Intelligence, Morgan Kaufmann, Chambery, 2018, pp. 2826\u20132832.","DOI":"10.24963\/ijcai.2018\/392"},{"key":"10.3233\/IDA-205393_ref25","doi-asserted-by":"crossref","first-page":"3008","DOI":"10.4028\/www.scientific.net\/AMR.926-930.3008","article-title":"Combining clustering and voting scheme to select initial training set for active learning","volume":"926\u2013930","author":"Leng","year":"2014","journal-title":"Advanced Materials Research"},{"key":"10.3233\/IDA-205393_ref26","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.knosys.2017.07.010","article-title":"Adaptive density peak clustering based on K-nearest neighbors with aggregating strategy","volume":"133","author":"Liu","year":"2017","journal-title":"Knowledge-Based Systems"},{"key":"10.3233\/IDA-205393_ref27","doi-asserted-by":"crossref","unstructured":"Y. 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