Abstract
We introduce an instance-weighting method to induce costsensitive trees in this paper. It is a generalization of the standard tree induction process where only the initial instance weights determine the type of tree to be induced—minimum error trees or minimum high cost error trees. We demonstrate that it can be easily adapted to an existing tree learning algorithm. Previous research gave insufficient evidence to support the fact that the greedy divide-and-conquer algorithm can effectively induce a truly cost-sensitive tree directly from the training data. We provide this empirical evidence in this paper. The algorithm incorporating the instance-weighting method is found to be better than the original algorithm in terms of total misclassification costs, the number of high cost errors and tree size in two-class datasets. The instanceweighting method is simpler and more effective in implementation than a previous method based on altered priors.
Chapter PDF
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Breiman, L., J.H. Friedman, R.A. Olshen & C.J. Stone (1984), Classification And Regression Trees, Belmont, CA: Wadsworth.
Knoll, U., Nakhaeizadeh, G., & Tausend, B. (1994), Cost-Sensitive Pruning of Decision Trees, in Proceedings of the Eighth European Conference on Machine Learning, pp. 383–386, Springer-Verlag.
Merz, C.J. & Murphy, P.M. (1996), UCI Repository of machine learning databases [http://www.ics.uci. edu/mlearn/MLRepository.html]. University of California, Dept. of Information and Computer Science.
Michie, D., D.J. Spiegelhalter & C.C. Taylor (1994), Machine Learning, Neural and Statistical Classification, Ellis Horwood Limited.
Pazzani, M., C. Merz, P. Murphy, K. Ali, T. Hume & C. Brunk (1994), Reducing Misclassification Costs, in Proceedings of the Eleventh International Conference on Machine Learning, pp. 217–225, Morgan Kaufmann.
Quinlan, J.R. (1993), C4.5: Program for machine learning, Morgan Kaufmann.
Quinlan, J.R. (1996), Boosting, Bagging, and C4.5, in Proceedings of the 13th National Conference on Artificial Intelligence, pp. 725–730, AAAI Press.
Schapire, R.E., Y. Freund, P. Bartlett & W.S. Lee (1997), Boosting the margin: A new explanation for the effectiveness of voting methods, in Proceedings of the Fourteenth International Conference on Machine Learning, pp. 322–330, Morgan Kaufmann.
Turney, P.D. (1995), Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm, Journal of Artificial Intelligence Research, 2, pp. 369–409.
Webb, G.I. (1996) Cost-Sensitive Specialization, in Proceedings of the 1996 Pacific Rim International Conference on Artificial Intelligence, pp. 23–34.
Ting, K.M. & Z. Zheng (1998), Boosting Trees for Cost-Sensitive Classifications, Proceedings of the Tenth European Conference on Machine Learning, Berlin: Springer-Verlag, pp. 190–195.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ting, K.M. (1998). Inducing cost-sensitive trees via instance weighting. In: Żytkow, J.M., Quafafou, M. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 1998. Lecture Notes in Computer Science, vol 1510. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0094814
Download citation
DOI: https://doi.org/10.1007/BFb0094814
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65068-3
Online ISBN: 978-3-540-49687-8
eBook Packages: Springer Book Archive