Boosting Cost-Sensitive Trees
Lecture notes in computer sciencePublished 1 January 1998
Kai Ming Ting, Zijian Zheng
Citations31
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
This paper explores two techniques for boosting cost-sensitive trees that differ in whether the misclassification cost information is utilized during training and provides a means to overcome the weaknesses of their base cost- sensitive tree induction algorithm.
Abstract
This paper explores two techniques for boosting costsensitive trees. The two techniques differ in whether the misclassification cost information is utilized during training. We demonstrate that each of these techniques is good at different aspects of cost-sensitive classifications. We also show that both techniques provide a means to overcome the weaknesses of their base cost-sensitive tree induction algorithm
Keywords
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