{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,3]],"date-time":"2022-04-03T15:32:02Z","timestamp":1648999922313},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2013,9,5]],"date-time":"2013-09-05T00:00:00Z","timestamp":1378339200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2014,12]]},"DOI":"10.1007\/s10115-013-0683-1","type":"journal-article","created":{"date-parts":[[2013,9,4]],"date-time":"2013-09-04T04:42:46Z","timestamp":1378269766000},"page":"871-892","source":"Crossref","is-referenced-by-count":2,"title":["PAKDD\u201912 best paper: generating balanced classifier-independent training samples from unlabeled data"],"prefix":"10.1007","volume":"41","author":[{"given":"Youngja","family":"Park","sequence":"first","affiliation":[]},{"given":"Zijie","family":"Qi","sequence":"additional","affiliation":[]},{"given":"Suresh N.","family":"Chari","sequence":"additional","affiliation":[]},{"given":"Ian M.","family":"Molloy","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2013,9,5]]},"reference":[{"issue":"1","key":"683_CR1","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1145\/1007730.1007737","volume":"6","author":"T Jo","year":"2004","unstructured":"Jo T, Japkowicz N (2004) Class imbalances versus small disjuncts. SIGKDD Explor 6(1):40\u201349","journal-title":"SIGKDD Explor"},{"key":"683_CR2","unstructured":"Weiss G, Provost F (2001) The effect of class distribution on classifier learning: an empirical study. Department of computer science, Rutgers University, technical report, ML-TR-43"},{"key":"683_CR3","doi-asserted-by":"crossref","unstructured":"Zadrozny B (2004) Learning and evaluating classifiers under sample selection bias. In: Proceedings of the twenty-first international conference on machine learning. ACM","DOI":"10.1145\/1015330.1015425"},{"key":"683_CR4","doi-asserted-by":"crossref","unstructured":"Dasgupta S, Hsu D (2008) Hierarchical sampling for active learning. In: Proceedings of the 25th international conference on machine learning. ACM","DOI":"10.1145\/1390156.1390183"},{"key":"683_CR5","doi-asserted-by":"crossref","unstructured":"Ertekin S, 0002 JH, Bottou L, Giles CL (2007) Learning on the border: active learning in imbalanced data classification. In: Proceedings of the sixteenth ACM conference on conference on information and knowledge management. ACM","DOI":"10.1145\/1321440.1321461"},{"key":"683_CR6","unstructured":"Settles B (2009) Active learning literature survey. University of Wisconsin-Madison, technical report"},{"key":"683_CR7","first-page":"937","volume":"6","author":"A Bar-Hillel","year":"2005","unstructured":"Bar-Hillel A, Hertz T, Shental N, Weinshall D (2005) Learning a mahalanobis metric from equivalence constraints. J Mach Learn Res 6:937\u2013965","journal-title":"J Mach Learn Res"},{"key":"683_CR8","unstructured":"Wagstaff K, Cardie C (2000) Clustering with instance-level constraints. In: Proceedings of the seventeenth international conference on machine learning. ACM, pp 1103\u20131110"},{"key":"683_CR9","first-page":"505","volume":"15","author":"EP Xing","year":"2002","unstructured":"Xing EP, Ng AY, Jordan MI, Russell S (2002) Distance metric learning, with application to clustering with side-information. Adv Neural Inf Process Syst 15:505\u2013512","journal-title":"Adv Neural Inf Process Syst"},{"key":"683_CR10","unstructured":"Provos N, Mavrommatis P, Rajab M, Monrose F (2008) All your iFRAMEs point to us. Google, technical report"},{"key":"683_CR11","unstructured":"Bache K, Lichman M (2013) UCI Machine Learning Repository. University of California, School of Information and Computer science, Irvine, CA. http:\/\/archive.ics.uci.edu\/ml"},{"key":"683_CR12","unstructured":"Liu XY, Wu J, Zhou ZH (2009) Exploratory undersampling for class imbalance learning. In: IEEE Transaction on systems, man and cybernetics"},{"key":"683_CR13","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Smote: synthetic minority over-sampling technique. JAIR 16:321\u2013357","journal-title":"JAIR"},{"key":"683_CR14","unstructured":"Wu Y, Zhang R, Rudnicky E (2007) Data selection for speech recognition. In: Automatic speech recognition & understanding, 2007, ASRU, IEEE workshop"},{"key":"683_CR15","unstructured":"Campbell C, Cristianini N, Smola AJ (2000) Query learning with large margin classifiers. In: Proceedings of the seventeenth international conference on machine learning. ACM, pp 111\u2013118"},{"issue":"2\u20133","key":"683_CR16","doi-asserted-by":"crossref","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\u20133):133\u2013168","journal-title":"Mach Learn"},{"key":"683_CR17","unstructured":"Tong S, Koller D (2000) Support vector machine active learning with applications to text classification. In: Proceedings of the seventeenth international conference on machine learning. ACM, pp 999\u20131006"},{"key":"683_CR18","doi-asserted-by":"crossref","unstructured":"Lewis DD, Gale WA (1994) A sequential algorithm for training text classifiers. In: Proceedings of the 17th annual international ACM SIGIR conference on research and development in information retrieval. Springer, New York","DOI":"10.1007\/978-1-4471-2099-5_1"},{"key":"683_CR19","doi-asserted-by":"crossref","unstructured":"Seung HS, Opper M, Sompolinsky H (1992) Query by committee. In: Proceedings of the fifth annual workshop on Computational learning theory. ACM","DOI":"10.1145\/130385.130417"},{"key":"683_CR20","doi-asserted-by":"crossref","unstructured":"Hoi SCH, Jin R, Zhu J, Lyu MR (2006) Batch mode active learning and its application to medical image classification. ICML","DOI":"10.1145\/1143844.1143897"},{"key":"683_CR21","unstructured":"Guo Y, Schuurmans D (2007) Discriminative batch mode active learning. In: Advances in neural information processing systems (NIPS) vol 20, pp 593\u2013600"},{"key":"683_CR22","unstructured":"Schohn G, Cohn D (2000) Less is more: active learning with support vector machines. In: Proceedings of the seventeenth international conference on machine learning. ACM, pp 839\u2013846"},{"key":"683_CR23","doi-asserted-by":"crossref","unstructured":"Xu Z, Hogan C, Bauer R (2009) Greedy is not enough: an efficient batch mode active learning algorithm. In: ICDM workshops","DOI":"10.1109\/ICDMW.2009.38"},{"key":"683_CR24","doi-asserted-by":"crossref","unstructured":"Tomanek K, Hahn U (2009) Reducing class imbalance during active learning for named entity recognition. In: Proceedings of the fifth international conference on Knowledge capture. ACM","DOI":"10.1145\/1597735.1597754"},{"key":"683_CR25","unstructured":"Zhu J, Hovy E (2007) Active learning for word sense disambiguation with methods for dddressing the class imbalance problem. EMNLP-CoNLL"},{"key":"683_CR26","unstructured":"Wagstaff K, Cardie C, Rogers S, Schr\u00f6dl S (2001) Constrained k-means clustering with background knowledge. In: Proceedings of the eighteenth international conference on machine learning. ACM, pp 577\u2013584"},{"key":"683_CR27","unstructured":"Basu S, Banerjee A, Mooney R (2002) Semi-supervised clustering by seeding. In: Proceedings of 19th international conference on, machine learning (ICML-2002)"},{"key":"683_CR28","doi-asserted-by":"crossref","unstructured":"Shental N, Hertz T, Weinshall D, Pavel M (2002) Adjustment learning and relevant component analysis. In: Proceedings of the 7th European conference on computer vision-Part IV, ser. ECCV \u201902, pp 776\u2013792","DOI":"10.1007\/3-540-47979-1_52"},{"issue":"3\u20134","key":"683_CR29","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/0025-5564(75)90047-4","volume":"23","author":"EH Shortliffe","year":"1975","unstructured":"Shortliffe EH, Buchanan BG (1975) A model of inexact reasoning in medicine. Math Biosci 23(3\u20134):351\u2013379","journal-title":"Math Biosci"},{"key":"683_CR30","doi-asserted-by":"crossref","unstructured":"Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379\u2013423","DOI":"10.1002\/j.1538-7305.1948.tb01338.x"},{"key":"683_CR31","doi-asserted-by":"crossref","DOI":"10.1002\/0471200611","volume-title":"Elements of information theory","author":"TM Cover","year":"1991","unstructured":"Cover TM, Thomas JA (1991) Elements of information theory. Wiley, New York"},{"key":"683_CR32","doi-asserted-by":"crossref","unstructured":"Mierswa I, Wurst M, Klinkenberg R, Scholz M, Euler T (2006) Yale: rapid prototyping for complex data mining tasks. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM","DOI":"10.1145\/1150402.1150531"},{"key":"683_CR33","unstructured":"Rifkin RM, Klautau A (2004) In defense of one-vs-all classification. J Mach Learn Res 5:101\u2013141"},{"key":"683_CR34","unstructured":"MacQueenu JB (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of 5-th Berkeley symposium on mathematical statistics and probability. University of California Berkeley Press, pp 281\u2013297"},{"key":"683_CR35","unstructured":"Ester M, peter Kriegel H, JS, Xu X, (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of knowledge discovery and data mining (KDD). AAAI Press, pp 226\u2013231"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-013-0683-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10115-013-0683-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-013-0683-1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,7,23]],"date-time":"2019-07-23T04:31:41Z","timestamp":1563856301000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10115-013-0683-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,9,5]]},"references-count":35,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2014,12]]}},"alternative-id":["683"],"URL":"https:\/\/doi.org\/10.1007\/s10115-013-0683-1","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,9,5]]}}}