The impact of changing populations on classifier performance

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The impact of changing populations on classifier performance

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International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining table of contents

San Diego, California, United States

Pages: 367 - 371

Year of Publication: 1999

ISBN:1-58113-143-7

Authors

Mark G. Kelly	Department of Mathematics, Imperial College, 180 Queen's Gate, London, SW7 2BZ, UK
David J. Hand	Department of Mathematics, Imperial College, 180 Queen's Gate, London, SW7 2BZ, UK
Niall M. Adams	Department of Mathematics, Imperial College, 180 Queen's Gate, London, SW7 2BZ, UK

Sponsors

SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
AAAI : Am Assoc for Artifical Intelligence
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGMOD: ACM Special Interest Group on Management of Data

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 7, Downloads (12 Months): 58, Citation Count: 12

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Adams N.M. and Hand D.J. (1999) Comparing classifters when the misallocation costs are uncertain. To appear in Pattern Recognition, 32.
	2	Henley W.E. (1995) Statistical aspects of credit scoring. Unpublished PhD thesis, The Open University, Milton Keynes, UK.
	3	Kelly M.G. and Hand D.J. (1999) Credit scoring with uncertain class definitions. To appear in IMA Journal of Mathematics Applied in Business and Industry.
	4	Lane T. and Brodley C.E. (1998) Approaches to online learning and concept drift for user identification in computer security. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, ed. R.Agrawal, P. Stolorz, and G.Piatetsky-Shapiro. AAAI Press, Menlo Park, California. 259-263.
	5	Nakhaeizadeh G., Taylor C.C., Kunisch G. (1997) Dynamic supervised learning: some basic issues and application aspects. Classification and Knowledge Organization, ed. R. Klar and O. Optiz, Berlin: Springer-Verlag, 123-135.
	6	Nakhaeizadeh, G., Taylor, C. and Lanquillon, C. (1998). Evaluating usefulness for dynamic classification. Knowledge Discovery and Data Mining KDD- 98. ed. R. Agrawal, P. Stolorz, G. Piatetsky-Shapiro. AAAi, 87-93.
	7	Taylor, C.C., Nakhaeizadeh, G., and Kunisch, G. (1997) Statistical aspects of classification in drifting populations. Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, Fort Lauderdale, 521-528.
	8	Gerhard Widmer , Miroslav Kubat, Learning in the presence of concept drift and hidden contexts, Machine Learning, v.23 n.1, p.69-101, April 1996 [doi>10.1023/A:1018046501280]

CITED BY 12

	Geoff Hulten , Laurie Spencer , Pedro Domingos, Mining time-changing data streams, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, p.97-106, August 26-29, 2001, San Francisco, California
	Mark Last, Online classification of nonstationary data streams, Intelligent Data Analysis, v.6 n.2, p.129-147, April 2002
	F. Fdez-Riverola , E. L. Iglesias , F. Díaz , J. R. Méndez , J. M. Corchado, Applying lazy learning algorithms to tackle concept drift in spam filtering, Expert Systems with Applications: An International Journal, v.33 n.1, p.36-48, July, 2007
	R. Salas , S. Moreno , H. Allende , C. Moraga, A robust and flexible model of hierarchical self-organizing maps for non-stationary environments, Neurocomputing, v.70 n.16-18, p.2744-2757, October, 2007
	Anand Narasimhamurthy , Ludmila I. Kuncheva, A framework for generating data to simulate changing environments, Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications, p.384-389, February 12-14, 2007, Innsbruck, Austria
	Rocío Alaiz-Rodríguez , Alicia Guerrero-Curieses , Jesús Cid-Sueiro, Minimax Regret Classifier for Imprecise Class Distributions, The Journal of Machine Learning Research, 8, p.103-130, 5/1/2007
	Sunita Sarawagi , Sree Hari Nagaralu, Data mining models as services on the internet, ACM SIGKDD Explorations Newsletter, v.2 n.1, p.24-28, June, 2000
	C. Whitrow , D. J. Hand , P. Juszczak , D. Weston , N. M. Adams, Transaction aggregation as a strategy for credit card fraud detection, Data Mining and Knowledge Discovery, v.18 n.1, p.30-55, February 2009
	Rick Pechter, Conformance standard for the predictive model markup language, Proceedings of the 4th international workshop on Data mining standards, services and platforms, p.6-13, August 20-20, 2006, Philadelphia, Pennsylvania
	Piotr Juszczak , Niall M. Adams , David J. Hand , Christopher Whitrow , David J. Weston, Off-the-peg and bespoke classifiers for fraud detection, Computational Statistics & Data Analysis, v.52 n.9, p.4521-4532, May, 2008
	J. Zico Kolter , Marcus A. Maloof, Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts, The Journal of Machine Learning Research, 8, p.2755-2790, 12/1/2007
	F. Fdez-Riverola , E. L. Iglesias , F. Díaz , J. R. Méndez , J. M. Corchado, SpamHunting: An instance-based reasoning system for spam labelling and filtering, Decision Support Systems, v.43 n.3, p.722-736, April, 2007