{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T19:03:01Z","timestamp":1725562981112},"publisher-location":"Berlin, Heidelberg","reference-count":26,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783642146398"},{"type":"electronic","value":"9783642146404"}],"license":[{"start":{"date-parts":[[2010,1,1]],"date-time":"2010-01-01T00:00:00Z","timestamp":1262304000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2010]]},"DOI":"10.1007\/978-3-642-14640-4_3","type":"book-chapter","created":{"date-parts":[[2010,8,14]],"date-time":"2010-08-14T02:41:52Z","timestamp":1281753712000},"page":"28-39","source":"Crossref","is-referenced-by-count":5,"title":["An Empirical Study of Applying Ensembles of Heterogeneous Classifiers on Imperfect Data"],"prefix":"10.1007","author":[{"given":"Kuo-Wei","family":"Hsu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaideep","family":"Srivastava","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"issue":"1","key":"3_CR1","first-page":"37","volume":"6","author":"D. Aha","year":"1991","unstructured":"Aha, D., Kibler, D.: Instance-based learning algorithms. Mach. Learn.\u00a06(1), 37\u201366 (1991)","journal-title":"Mach. Learn."},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Aksela, M.: Comparison of Classifier Selection Methods for Improving Committee Performance. MCS, 84\u201393 (2003)","DOI":"10.1007\/3-540-44938-8_9"},{"key":"3_CR3","unstructured":"Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. School of Information and Computer Science. University of California, Irvine (2007)"},{"issue":"1","key":"3_CR4","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.inffus.2004.04.005","volume":"6","author":"R.E. Banfield","year":"2005","unstructured":"Banfield, R.E., Hall, L.O., Bowyer, K.W., Kegelmeyer, W.P.: Ensemble Diversity Measures and their Application to Thinning. Information Fusion J.\u00a06(1), 49\u201362 (2005)","journal-title":"Information Fusion J."},{"issue":"1","key":"3_CR5","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1109\/TPAMI.2007.250609","volume":"29","author":"R.E. Banfield","year":"2007","unstructured":"Banfield, R.E., Hall, L.O., Bowyer, K.W., Kegelmeyer, W.P.: A Com-parison of De-cision Tree Ensemble Creation Techniques. IEEE Trans. on Pattern Analysis and Machine Intelligence\u00a029(1), 173\u2013180 (2007)","journal-title":"IEEE Trans. on Pattern Analysis and Machine Intelligence"},{"key":"3_CR6","unstructured":"Banfield, R.E., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Boosting Lite - Handling Larger data sets and Slower Base Classifiers. MCS (2007)"},{"key":"3_CR7","unstructured":"Chawla, N., Moore, T., Bowyer, K., Hall, L., Springer, C., Kegelmeyer, P.: Investigation of bagging-like effects and decision trees versus neural nets in protein secondary structure prediction. In: Workshop on Data Min-ing in Bioinformatics, KDD (2001)"},{"issue":"1","key":"3_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1007730.1007733","volume":"6","author":"N. Chawla","year":"2004","unstructured":"Chawla, N., Japkowicz, N., Kolcz, A.: Editorial: Special Issue on Learn-ing from Imbalanced data sets. SIGKDD Expl.\u00a06(1), 1\u20136 (2004)","journal-title":"SIGKDD Expl."},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Cieslak, D., Chawla, N.: Start Globally, Optimize Locally, Predict Globally: Improving Performance on Unbalanced Data. In: ICDM (2008)","DOI":"10.1109\/ICDM.2008.87"},{"key":"3_CR10","first-page":"18","volume":"1","author":"T.-G. Fan","year":"2008","unstructured":"Fan, T.-G., Zhu, Y., Chen, J.-M.: A new measure of classifier diversity in multiple classifier system. ICMLC\u00a01, 18\u201321 (2008)","journal-title":"ICMLC"},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Forman, G., Cohen, I.: Learning from Little: Comparison of Classifiers Given Little Training. ECML. HPL-2004-19R1 (2004)","DOI":"10.1007\/978-3-540-30116-5_17"},{"issue":"1","key":"3_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1214\/aos\/1176347963","volume":"19","author":"J.H. Friedman","year":"1991","unstructured":"Friedman, J.H.: Multivariate adaptive regression splines. Ann. of Stat.\u00a019(1), 1\u201367 (1991)","journal-title":"Ann. of Stat."},{"key":"3_CR13","unstructured":"Hettich, S., Bay, S.D.: The UCI KDD Archive. Department of Informa-tion and Computer Science. University of California, Irvine (1999)"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Hsu, K.-W., Srivastava, J.: Diversity in Combinations of Heterogeneous Classifiers. In: PAKDD (2009)","DOI":"10.1007\/978-3-642-01307-2_97"},{"key":"3_CR15","unstructured":"John, G.H., Langley, P.: Estimating Continuous Distributions in Bayesian Classifiers. In: Conference on Uncertainty in Artificial Intelligence, pp. 338\u2013345 (1995)"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer Series in Information Sciences, vol. 30 (2001)","DOI":"10.1007\/978-3-642-56927-2"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Kuncheva, L.I., Whitaker, C.J.: 10 measures of diversity in classifier ensembles: limits for two classifiers. In: A DERA\/IEE Workshop on Intelligent Sensor Processing, pp. 10\/1\u201310\/10 (2001)","DOI":"10.1049\/ic:20010105"},{"issue":"2","key":"3_CR18","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1023\/A:1022859003006","volume":"51","author":"L.I. Kuncheva","year":"2003","unstructured":"Kuncheva, L.I., Whitaker, C.J.: Measures of Diversity in Classifier En-sembles and Their Relationship with the Ensemble Accuracy. Mach. Learn.\u00a051(2), 181\u2013207 (2003)","journal-title":"Mach. Learn."},{"key":"3_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"1126","DOI":"10.1007\/978-3-540-44871-6_130","volume-title":"Pattern Recognition and Image Analysis","author":"L.I. Kuncheva","year":"2003","unstructured":"Kuncheva, L.I.: That elusive diversity in classifier ensembles. In: Perales, F.J., Campilho, A.C., P\u00e9rez, N., Sanfeliu, A. (eds.) IbPRIA 2003. LNCS, vol.\u00a02652, pp. 1126\u20131138. Springer, Heidelberg (2003)"},{"key":"3_CR20","doi-asserted-by":"crossref","unstructured":"Liu, X.-Y., Wu, J., Zhou, Z.-H.: Exploratory under-sampling for class-imbalance learning. In: ICDM, pp. 965\u2013969 (2006)","DOI":"10.1109\/ICDM.2006.68"},{"key":"3_CR21","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1016\/S0950-5849(97)00023-2","volume":"39","author":"D. Partridge","year":"1997","unstructured":"Partridge, D., Krzanowski, W.J.: Software diversity: practical statistics for its meas-urement and exploitation. Information and Software Technol-ogy\u00a039, 707\u2013717 (1997)","journal-title":"Information and Software Technol-ogy"},{"key":"3_CR22","unstructured":"Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Francisco (1993)"},{"issue":"1","key":"3_CR23","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1145\/1007730.1007734","volume":"6","author":"G.M. Weiss","year":"2004","unstructured":"Weiss, G.M.: Mining with Rarity: A Unifying Framework. SIGKDD Expl.\u00a06(1), 7\u201319 (2004)","journal-title":"SIGKDD Expl."},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"Weiss, G.M.: Mining Rare Cases. In: O. Data Mining and Knowledge Discovery Hand-book: A Complete Guide for Practitioners and Research-ers, pp. 765\u2013776 (2005)","DOI":"10.1007\/0-387-25465-X_35"},{"key":"3_CR25","volume-title":"Data Mining: Practical machine learning tools and techniques","author":"I.H. Witten","year":"2005","unstructured":"Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)","edition":"2"},{"key":"3_CR26","unstructured":"Whitaker, C.J., Kuncheva, L.I.: Examining the relationship between ma-jority vote accuracy and diversity in bagging and boosting, Technical Re-port, School of Informatics, University of Wales, Bangor (2003)"}],"container-title":["Lecture Notes in Computer Science","New Frontiers in Applied Data Mining"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-642-14640-4_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,19]],"date-time":"2019-05-19T17:13:34Z","timestamp":1558286014000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-642-14640-4_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2010]]},"ISBN":["9783642146398","9783642146404"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-642-14640-4_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2010]]}}}