Cost-Sensitive Decision Trees with Pre-pruning

Authors:
Jun Du;Zhihua Cai;Charles X. Ling
Affiliations:
School of Computer Science, China University of Geosciences, Wuhan, 430074, P.R. China;School of Computer Science, China University of Geosciences, Wuhan, 430074, P.R. China and Dept. of Computer Science, The University of Western Ontario, London, Ontario, N6A 5B7, Canada;Dept. of Computer Science, The University of Western Ontario, London, Ontario, N6A 5B7, Canada
Venue:
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Year:
2007

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Abstract

This paper explores two simple and efficient pre-pruning strategies for the cost-sensitive decision tree algorithm to avoid overfitting. One is to limit the cost-sensitive decision trees to a depth of two. The other is to prune the trees with a pre-specified threshold. Empirical study shows that, compared to the error-based tree algorithm C4.5 and several other cost-sensitive tree algorithms, the new cost-sensitive decision trees with pre-pruning are more efficient and perform well on most UCI data sets.