{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T17:57:05Z","timestamp":1725559025570},"publisher-location":"Berlin, Heidelberg","reference-count":27,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783642142734"},{"type":"electronic","value":"9783642142741"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2010]]},"DOI":"10.1007\/978-3-642-14274-1_16","type":"book-chapter","created":{"date-parts":[[2010,7,9]],"date-time":"2010-07-09T05:32:53Z","timestamp":1278653573000},"page":"201-212","source":"Crossref","is-referenced-by-count":2,"title":["Detecting Change via Competence Model"],"prefix":"10.1007","author":[{"given":"Ning","family":"Lu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangquan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"16_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1007\/3-540-56602-3_139","volume-title":"Machine Learning: ECML-93","author":"G. Widmer","year":"1993","unstructured":"Widmer, G., Kubat, M.: Effective learning in dynamic environments by explicit context tracking. In: Brazdil, P.B. (ed.) ECML 1993. LNCS, vol.\u00a0667, pp. 227\u2013243. Springer, Heidelberg (1993)"},{"issue":"1","key":"16_CR2","first-page":"69","volume":"23","author":"G. Widmer","year":"1996","unstructured":"Widmer, G., Kubat, M.: Learning in the Presence of Concept Drift and Hidden Contexts. Machine Learning\u00a023(1), 69\u2013101 (1996)","journal-title":"Machine Learning"},{"key":"16_CR3","first-page":"97","volume-title":"7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"G. Hulten","year":"2001","unstructured":"Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 97\u2013106. ACM Press, San Francisco (2001)"},{"issue":"4","key":"16_CR4","doi-asserted-by":"publisher","first-page":"1283","DOI":"10.1016\/j.asoc.2007.11.003","volume":"8","author":"L. Cohen","year":"2008","unstructured":"Cohen, L., Avrahami, G., Last, M., Kandel, A.: Info-fuzzy algorithms for mining dynamic data streams. Applied Soft Computing\u00a08(4), 1283\u20131294 (2008)","journal-title":"Applied Soft Computing"},{"key":"16_CR5","unstructured":"Tsymbal, A.: The Problem of Concept Drift: Definitions and Related Work. Technical Re-port TCD-CS-2004-15, Department of Computer Science, Trinity College Dublin, Ireland (2004)"},{"issue":"2","key":"16_CR6","doi-asserted-by":"publisher","first-page":"1164","DOI":"10.1016\/j.eswa.2007.11.034","volume":"36","author":"C.-J. Tsai","year":"2009","unstructured":"Tsai, C.-J., Lee, C.-I., Yang, W.-P.: Mining decision rules on data streams in the presence of concept drifts. Expert Syst. Appl.\u00a036(2), 1164\u20131178 (2009)","journal-title":"Expert Syst. Appl."},{"issue":"1-2","key":"16_CR7","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.artint.2003.04.001","volume":"154","author":"M.A. Maloof","year":"2004","unstructured":"Maloof, M.A., Michalski, R.S.: Incremental learning with partial instance memory. Artificial Intelligence\u00a0154(1-2), 95\u2013126 (2004)","journal-title":"Artificial Intelligence"},{"issue":"4-5","key":"16_CR8","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.knosys.2004.10.002","volume":"18","author":"S.J. Delany","year":"2005","unstructured":"Delany, S.J., Cunningham, P., Tsymbal, A., Coyle, L.: A case-based technique for tracking concept drift in spam filtering. Knowledge-Based Systems\u00a018(4-5), 187\u2013195 (2005)","journal-title":"Knowledge-Based Systems"},{"issue":"3","key":"16_CR9","doi-asserted-by":"crossref","first-page":"281","DOI":"10.3233\/IDA-2004-8305","volume":"8","author":"R. Klinkenberg","year":"2004","unstructured":"Klinkenberg, R.: Learning drifting concepts: Example selection vs. example weighting. Intell. Data Anal.\u00a08(3), 281\u2013300 (2004)","journal-title":"example weighting. Intell. Data Anal."},{"key":"16_CR10","first-page":"377","volume-title":"7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"W.N. Street","year":"2001","unstructured":"Street, W.N., Kim, Y.: A streaming ensemble algorithm (SEA) for large-scale classification. In: 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 377\u2013382. ACM Press, San Francisco (2001)"},{"key":"16_CR11","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1007\/3-540-36175-8","volume-title":"9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"H. Wang","year":"2003","unstructured":"Wang, H., Fan, W., Yu, P.S., Han, J.: Mining concept-drifting data streams using ensemble classifiers. In: 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 226\u2013235. ACM Press, Washington (2003)"},{"key":"16_CR12","first-page":"2755","volume":"8","author":"J.Z. Kolter","year":"2007","unstructured":"Kolter, J.Z., Maloof, M.A.: Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts. J. Mach. Learn. Res.\u00a08, 2755\u20132790 (2007)","journal-title":"J. Mach. Learn. Res."},{"key":"16_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1021","DOI":"10.1007\/978-3-642-01307-2_109","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"P. Zhang","year":"2009","unstructured":"Zhang, P., Zhu, X., Shi, Y., Wu, X.: An Aggregate Ensemble for Mining Concept Drifting Data Streams with Noise. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS, vol.\u00a05476, pp. 1021\u20131029. Springer, Heidelberg (2009)"},{"issue":"1","key":"16_CR14","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.inffus.2006.11.002","volume":"9","author":"A. Tsymbal","year":"2008","unstructured":"Tsymbal, A., Pechenizkiy, M., Cunningham, P., Puuronen, S.: Dynamic integration of classifiers for handling concept drift. Information Fusion\u00a09(1), 56\u201368 (2008)","journal-title":"Information Fusion"},{"key":"16_CR15","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1145\/1014052.1014069","volume-title":"10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"W. Fan","year":"2004","unstructured":"Fan, W.: Systematic data selection to mine concept-drifting data streams. In: 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 128\u2013137. ACM Press, Seattle (2004)"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Kifer, D., Ben-David, S., Gehrke, J.: Detecting change in data streams. In: 13th International Conference on Very Large Data Bases. VLDB Endowment, Toronto, Canada, pp. 180\u2013191 (2004)","DOI":"10.1016\/B978-012088469-8.50019-X"},{"key":"16_CR17","first-page":"286","volume-title":"17th Brazilian Symposium on Artificial Intelligence","author":"J. Gama","year":"2004","unstructured":"Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with Drift Detection. In: 17th Brazilian Symposium on Artificial Intelligence, pp. 286\u2013295. Springer, Sao Luis (2004)"},{"key":"16_CR18","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1007\/978-3-540-75488-6_27","volume-title":"10th International Conference on Discovery Science","author":"K. Nishida","year":"2007","unstructured":"Nishida, K., Yamauchi, K.: Detecting Concept Drift Using Statistical Testing. In: 10th International Conference on Discovery Science, pp. 264\u2013269. Springer, Heidelberg (2007)"},{"key":"16_CR19","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1145\/1281192.1281264","volume-title":"13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"X. Song","year":"2007","unstructured":"Song, X., Wu, M., Jermaine, C., Ranka, S.: Statistical change detection for multi-dimensional data. In: 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 667\u2013676. ACM Press, San Jose (2007)"},{"issue":"5-6","key":"16_CR20","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1002\/sam.10054","volume":"2","author":"A. Dries","year":"2009","unstructured":"Dries, A., R\u00fcckert, U.: Adaptive concept drift detection. Statistical Analysis and Data Mining\u00a02(5-6), 311\u2013327 (2009)","journal-title":"Statistical Analysis and Data Mining"},{"key":"16_CR21","unstructured":"Massie, S., Craw, S., Wiratunga, N.: What is CBR competence? BCS-SGAI Expert Update\u00a08(1), 7\u201310 (2005)"},{"key":"16_CR22","first-page":"377","volume-title":"14th International Joint Conference on Arti-ficial Intelligence","author":"B. Smyth","year":"1995","unstructured":"Smyth, B., Keane, M.T.: Remembering To Forget: A Competence-Preserving Case Deletion Policy for Case-Based Reasoning Systems. In: 14th International Joint Conference on Arti-ficial Intelligence, pp. 377\u2013382. Morgan Kaufmann, Montreal (1995)"},{"key":"16_CR23","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1007\/3-540-48508-2_25","volume-title":"3rd International Conference on Case-Based Reasoning and Development","author":"B. Smyth","year":"1999","unstructured":"Smyth, B., McKenna, E.: Footprint-Based Retrieval. In: 3rd International Conference on Case-Based Reasoning and Development, pp. 343\u2013357. Springer, Seeon Monastery (1999)"},{"issue":"2","key":"16_CR24","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1111\/0824-7935.00142","volume":"17","author":"B. Smyth","year":"2001","unstructured":"Smyth, B., McKenna, E.: Competence Models and the Maintenance Problem. Computational Intelligence\u00a017(2), 235\u2013249 (2001)","journal-title":"Computational Intelligence"},{"key":"16_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1007\/978-3-642-10684-2_35","volume-title":"ICONIP 2009","author":"N. Lu","year":"2009","unstructured":"Lu, N., Lu, J., Zhang, G.: Maintaining Footprint-Based Retrieval for Case Deletion. In: Leung, C.S., Lee, M., Chan, J.H. (eds.) ICONIP 2009, Part II. LNCS, vol.\u00a05864, pp. 318\u2013325. Springer, Heidelberg (2009)"},{"key":"16_CR26","first-page":"143","volume-title":"7th IEEE International Conference on Data Mining","author":"J. Gao","year":"2007","unstructured":"Gao, J., Fan, W., Han, J.: On Appropriate Assumptions to Mine Data Streams: Analysis and Practice. In: 7th IEEE International Conference on Data Mining, pp. 143\u2013152. IEEE Computer Society, Omaha (2007)"},{"key":"16_CR27","unstructured":"Stanley, K.O.: Learning concept drift with a committee of decision trees. Technical Report UT-AI-TR-03-302, Department of Computer Science, University of Texas at Austin, USA (2003)"}],"container-title":["Lecture Notes in Computer Science","Case-Based Reasoning. Research and Development"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-642-14274-1_16.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,30]],"date-time":"2021-04-30T12:23:00Z","timestamp":1619785380000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-642-14274-1_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2010]]},"ISBN":["9783642142734","9783642142741"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-642-14274-1_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2010]]}}}