{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T13:35:50Z","timestamp":1760708150469},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2010,1,22]],"date-time":"2010-01-22T00:00:00Z","timestamp":1264118400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2010,3]]},"DOI":"10.1007\/s10618-009-0146-1","type":"journal-article","created":{"date-parts":[[2010,1,20]],"date-time":"2010-01-20T21:47:46Z","timestamp":1264024066000},"page":"191-220","source":"Crossref","is-referenced-by-count":40,"title":["COG: local decomposition for rare class analysis"],"prefix":"10.1007","volume":"20","author":[{"given":"Junjie","family":"Wu","sequence":"first","affiliation":[]},{"given":"Hui","family":"Xiong","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2010,1,22]]},"reference":[{"key":"146_CR1","doi-asserted-by":"crossref","unstructured":"Boser B, Guyon I, Vapnik V (1992) A training algorithm for optimal margin classifiers. In: Proceedings of the 5th annual workshop on computational learning theory, pp 144\u2013152","DOI":"10.1145\/130385.130401"},{"key":"146_CR2","unstructured":"Chang C-C, Lin C-J (2001) LIBSVM\u2014a library for support vector machines. http:\/\/www.csie.ntu.edu.tw\/~cjlin\/libsvm\/"},{"key":"146_CR3","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"N Chawla","year":"2002","unstructured":"Chawla N, Bowyer K, Hall L, Kegelmeyer W (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16: 321\u2013357","journal-title":"J Artif Intell Res"},{"key":"146_CR4","doi-asserted-by":"crossref","unstructured":"Cohen W (1995) Fast effective rule induction. In: Proceedings of the 12th international conference on machine learning, pp 115\u2013123","DOI":"10.1016\/B978-1-55860-377-6.50023-2"},{"key":"146_CR5","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511801389","volume-title":"An introduction to support vector machines and other kernel-based learning methods","author":"N Cristianini","year":"2000","unstructured":"Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge"},{"key":"146_CR6","volume-title":"Probability and statistics","author":"M DeGroot","year":"2001","unstructured":"DeGroot M, Schervish M (2001) Probability and statistics, 3rd edn. Addison Wesley, Reading, MA","edition":"3"},{"key":"146_CR7","doi-asserted-by":"crossref","unstructured":"Domingos P (1999) MetaCost: a general method for making classifiers cost-sensitive. In: Proceedings of the 5th ACM SIGKDD international conference on knowledge discovery and data mining, pp 155\u2013164","DOI":"10.1145\/312129.312220"},{"key":"146_CR8","unstructured":"Drummond C, Holte R (2003) C4.5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling. In: Proceedings of the 20th international conference on machine learning, workshop on learning from imbalanced data sets II"},{"key":"146_CR9","volume-title":"Pattern classification and scene analysis","author":"R Duda","year":"1973","unstructured":"Duda R, Hart P (1973) Pattern classification and scene analysis. Wiley, New York"},{"key":"146_CR10","volume-title":"Pattern classification","author":"R Duda","year":"2000","unstructured":"Duda R, Hart P, Stork D (2000) Pattern classification, 2nd edn. Wiley, New York","edition":"2"},{"key":"146_CR11","unstructured":"Elkan C (2001) The foundations of cost-sensitive learning. In: Proceedings of the 2001 international joint conferences on artificial intelligence, pp 973\u2013978"},{"key":"146_CR12","unstructured":"Fan W, Stolfo S, Zhang J, Chan P (1999) AdaCost: misclassification cost-sensitive boosting. In: Proceedings of the 16th internation conference on machine learning, pp 97\u2013105"},{"key":"146_CR13","doi-asserted-by":"crossref","unstructured":"Freund Y, Schapire R (1995) A decision-theoretic generalization of on-line learning and an application to boosting. In: Proceedings of the 2nd European conference on computational learning Theory, pp 23\u201337","DOI":"10.1007\/3-540-59119-2_166"},{"key":"146_CR14","unstructured":"Genkin A, Lewis D, Madigan D (2005) BMR: Bayesian multinomial regression software. http:\/\/www.stat.rutgers.edu\/~madigan\/BMR\/"},{"key":"146_CR15","doi-asserted-by":"crossref","unstructured":"Han E-H, Boley D, Gini M, Gross R, Hastings K, Karypis G, Kumar V, Mobasher B, Moore J (1998) WebAce: a web agent for document categorization and exploration. In: Proceedings of the 2nd international conference on autonomous agents","DOI":"10.1145\/280765.280872"},{"key":"146_CR16","unstructured":"Japkowicz N (2002) Supervised learning with unsupervised output separation. In: Proceedings of the 6th international conference on artificial intelligence and soft computing, pp 321\u2013325"},{"key":"146_CR17","doi-asserted-by":"crossref","unstructured":"Joshi M, Agarwal R, Kumar V (2001a) Mining needle in a haystack: classifying rare classes via two-phase rule induction. In: Proceedings of the 2001 ACM SIGMOD international conference on management of data, pp 91\u2013102","DOI":"10.1145\/375663.375673"},{"key":"146_CR18","doi-asserted-by":"crossref","unstructured":"Joshi M, Kumar V, Agarwal R (2001b) Evaluating boosting algorithms to classify rare classes: comparison and improvements. In: Proceedings of the 2001 IEEE international conference on data mining, pp 257\u2013264","DOI":"10.1109\/ICDM.2001.989527"},{"key":"146_CR19","unstructured":"Karypis G (2003) CLUTO\u2014software for clustering high-dimensional datasets, Version 2.1.1. http:\/\/glaros.dtc.umn.edu\/gkhome\/views\/cluto"},{"key":"146_CR20","doi-asserted-by":"crossref","unstructured":"Kubat M, Holte R, Matwin S (1997) Learning when negative examples abound. In: Proceedings of the 9th European conference on machine learning, pp 146\u2013153","DOI":"10.1007\/3-540-62858-4_79"},{"key":"146_CR21","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1023\/A:1007452223027","volume":"30","author":"M Kubat","year":"1998","unstructured":"Kubat M, Holte R, Matwin S (1998) Machine learning for the detection of oil spills in satellite radar images. Mach Learn 30: 195\u2013215","journal-title":"Mach Learn"},{"key":"146_CR22","unstructured":"Kubat M, Matwin S (1997) Addressing the curse of imbalanced training sets: one-sided selection. In: Proceedings of the 14th international conference on machine learning, pp 179\u2013186"},{"key":"146_CR23","unstructured":"Ling C, Li C (1998) Data mining for direct marketing: problems and solutions. In: Proceedings of the 4th ACM SIGKDD international conference on knowledge discovery and data mining, pp 73\u201379"},{"key":"146_CR24","unstructured":"MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley symposium on mathematical statistics and probability, pp 281\u2013297"},{"key":"146_CR25","volume-title":"The data mining and knowledge discovery handbook","year":"2005","unstructured":"Maimon O, Rokach L (eds) (2005) The data mining and knowledge discovery handbook. Springer, Berlin"},{"key":"146_CR26","unstructured":"Margineantu D, Dietterich T (1999) Learning decision trees for loss minimization in multi-class problems. In: TR 99-30-03"},{"key":"146_CR27","unstructured":"Newman D, Hettich S, Blake C, Merz C (1998) UCI repository of machine learning databases. http:\/\/www.ics.uci.edu\/~mlearn\/MLRepository.html"},{"issue":"3","key":"146_CR28","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1108\/eb046814","volume":"14","author":"M Porter","year":"1980","unstructured":"Porter M (1980) An algorithm for suffix stripping. Program 14(3): 130\u2013137","journal-title":"Program"},{"key":"146_CR29","unstructured":"Quinlan R (1992) C4.5 Release 8. http:\/\/www.rulequest.com\/Personal\/"},{"issue":"3","key":"146_CR30","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1109\/34.75512","volume":"13","author":"S Raudys","year":"1991","unstructured":"Raudys S, Jain A (1991) Small sample size effects in statistical pattern recognition: recommendations for practitioners. IEEE Trans Pattern Anal Mach Intell 13(3): 252\u2013264","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"146_CR31","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1080\/08839519408945435","volume":"8","author":"P Riddle","year":"1994","unstructured":"Riddle P, Segal R, Etzioni O (1994) Representation design and brute-force induction in a boeing manufacturing design. Appl Artif Intell 8: 125\u2013147","journal-title":"Appl Artif Intell"},{"key":"146_CR32","volume-title":"Introduction to data mining","author":"P-N Tan","year":"2005","unstructured":"Tan P-N, Steinbach M, Kumar V (2005) Introduction to data mining. Addison-Wesley, Reading, MA"},{"key":"146_CR33","unstructured":"TREC (2000) Text retrieval conference. http:\/\/trec.nist.gov"},{"key":"146_CR34","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4757-2440-0","volume-title":"The nature of statistical learning","author":"V Vapnik","year":"1995","unstructured":"Vapnik V (1995) The nature of statistical learning. Springer\u2013Verlag, New York"},{"issue":"1","key":"146_CR35","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1145\/1007730.1007734","volume":"6","author":"G Weiss","year":"2004","unstructured":"Weiss G (2004) Mining with rarity: a unifying framework. ACM SIGKDD Explor 6(1): 7\u201319","journal-title":"ACM SIGKDD Explor"},{"key":"146_CR36","unstructured":"Weiss G, Hirsh H (1998) Learning to predict rare events in event sequences. In: Proceedings of the 4th ACM SIGKDD international conference on knowledge discovery and data mining, pp 359\u2013363"},{"key":"146_CR37","volume-title":"Data mining: practical machine learning tools and techniques","author":"I Witten","year":"2005","unstructured":"Witten I, Frank E (2005) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, San Francisco"},{"key":"146_CR38","doi-asserted-by":"crossref","unstructured":"Wu J, Xiong H, Wu P, Chen J (2007) Local decomposition for rare class analysis. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, pp 814\u2013823","DOI":"10.1145\/1281192.1281279"},{"key":"146_CR39","doi-asserted-by":"crossref","unstructured":"Xiong H, Wu J, Chen J (2006) K-means clustering versus validation measures: a data distribution perspective. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, pp 779\u2013784","DOI":"10.1145\/1150402.1150503"},{"key":"146_CR40","doi-asserted-by":"crossref","unstructured":"Zadrozny B, Langford J, Abe N (2003) Cost-sensitive learning by cost-proportionate example weighting. In: Proceedings of the 2003 IEEE international conference on data mining, pp 435\u2013442","DOI":"10.1109\/ICDM.2003.1250950"},{"key":"146_CR41","unstructured":"Zurada J, Foster B, Ward T (2001) Investigation of artificial neural networks for classifying levels of financial distress of firms: the case of an unbalanced training sample. In: Knowledge discovery for business information systems, pp 397\u2013423"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-009-0146-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10618-009-0146-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-009-0146-1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,30]],"date-time":"2019-05-30T15:29:40Z","timestamp":1559230180000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10618-009-0146-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2010,1,22]]},"references-count":41,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2010,3]]}},"alternative-id":["146"],"URL":"https:\/\/doi.org\/10.1007\/s10618-009-0146-1","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"value":"1384-5810","type":"print"},{"value":"1573-756X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2010,1,22]]}}}