Cost-Sensitive decision trees with multiple cost scales

Authors:
Zhenxing Qin;Shichao Zhang;Chengqi Zhang
Affiliations:
Faculty of Information Technology, University of Technology, Sydney, Sydney, NSW, Australia;Faculty of Information Technology, University of Technology, Sydney, Sydney, NSW, Australia;Faculty of Information Technology, University of Technology, Sydney, Sydney, NSW, Australia
Venue:
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Year:
2004

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Abstract

How to minimize misclassification errors has been the main focus of Inductive learning techniques, such as CART and C4.5 However, misclassification error is not the only error in classification problem Recently, researchers have begun to consider both test and misclassification costs Previous works assume the test cost and the misclassification cost must be defined on the same cost scale However, sometimes we may meet difficulty to define the multiple costs on the same cost scale In this paper, we address the problem by building a cost-sensitive decision tree by involving two kinds of cost scales, that minimizes the one kind of cost and control the other in a given specific budget Our work will be useful for many diagnostic tasks involving target cost minimization and resource consumption for obtaining missing information.