Abstract
In many application areas where databases are mined for classification rules, the latter may be subject to concept drift, that is change over time. Mining without taking this into account can result in severe degradation of the acquired classifier’s performance. This is especially the case when mining is conducted incrementally to maintain knowledge used by an on-line system. The TSAR methodology detects and copes with drift in such situations through the use of a time stamp attribute, applied to incoming batches of data, as an integral part of the mining process. Here we extend the use of TSAR by employing more refined time stamps: first to individual batches, then to individual examples within a batch. We develop two new decision tree based TSAR algorithms, CD4 and CD5 and compare these to our original TSAR algorithm CD3.
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References
Black, M., Hickey, R.J.: Maintaining the Performance of a Learned Classifier under Concept Drift. Intelligent Data Analysis 3 (1999) 453–474
Kelly, M.G., Hand, D.J., Adams, N.M.: The Impact of Changing Populations on Classifier Performance. In: Chaudhuri, S., Madigan, D. (eds.): Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, New York (1999) 367–371
Klenner, M., Hahn, U.: (1994). Concept Versioning: A Methodology for Tracking Evolutionary Concept Drift in Dynamic Concept Systems. In: Proceedings of Eleventh European Conference on Artificial Intelligence, Wiley, Chichester, England, 473–477
Schlimmer, J.C., Granger, R.H.: Incremental Learning from Noisy Data. Machine Learning 1 (1986) 317–354
Hembold,_D.P., Long, P.M.: Tracking Drifting Concepts by Minimising Disagreements. Machine Learning 14 (1994) 27–45
Widmer, G.: Tracking Changes through Meta-Learning. Machine Learning 27 (1997) 259–286
Widmer, G., Kubat, M.: Learning in the Presence of Concept Drift and Hidden Contexts. Machine Learning 23 (1996) 69–101
Chakrabarti, S., Sarawagi, S., Dom, B.: Mining Surprising Patterns using Temporal Description Length. In: Gupta, A., Shmueli, O., Widom, J. (eds.): Proceedings of the Twenty-Fourth International Conference on Very Large databases. Morgan Kaufmann, San Mateo, California (1998) 606–61
Chen, X., Petrounias, I.: Mining Temporal Features in Association Rules. In: Zytkow, J., Rauch, J. (eds,): Proceedings. of the Third European Conference on Principles and Practice of Knowledge Discovery in Databases. Lecture Notes in Artificial Intelligence, Vol. 1704. Springer-Verlag, Berlin Heidelberg New York (1999) 295–300
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, California (1993)
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© 2001 Springer-Verlag Berlin Heidelberg
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Hickey, R.J., Black, M.M. (2001). Refined Time Stamps for Concept Drift Detection During Mining for Classification Rules. In: Roddick, J.F., Hornsby, K. (eds) Temporal, Spatial, and Spatio-Temporal Data Mining. TSDM 2000. Lecture Notes in Computer Science(), vol 2007. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45244-3_3
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DOI: https://doi.org/10.1007/3-540-45244-3_3
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