CMDTL1 : Combining Multiple Classifiers into a Cost-Sensitive Decision Tree
نویسندگان
چکیده
For many real-life applications, such as medical diagnosis, cost of a decision is an important practical criterion which can not be ignored. The state-of-the-art C4.5 algorithm for inductive learning was not developed with this criterion in mind. However, some well-developed approaches exist that induce decision tree, giving importance to the cost criterion. This paper presents a general framework, CMDTL1 (Cost-sensitive Multi-method Decision Tree Learning 1), that combines multiple classifiers into a cost-effective decision tree. Empirical evaluation of CMDTL1 combining Tan & Schlimmers and Nunezs algorithms shows that it outperforms significantly its predecessors with respect to cost of decision, accuracy and accuracy/cost ratio. Thus it is shown here that a significant synergy can be obtained by combining multiple cost-sensitive classifiers.
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