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- W138371386 abstract "Decision tree technology has been proven to be a valuable way of capturing human decision making within a computer. One main problem for many traditional decision tree pruning methods is that it is always assumed that all misclassifications are equally probable and equally serious. However, in a real-world classification problem, there may be a cost associated with misclassifying examples from each class. Cost-sensitive classification allows one to assign different costs to different types of misclassifications. But few studies deal with how to decide misclassification cost. In this paper, we introduce a cost-sensitive decision tree pruning algorithm CC4.5 which is based on the C4.5 algorithm (Quinlan 1993) and uses expert knowledge to define misclassification costs. This paper is outlined as follows: Section 2 illustrates three different cost-sensitive pruning methods implemented in CC4.5. In section 3, we describe how we use expert knowledge to define the misclassification costs. The evaluation of CC4.5 is done via a comparative analysis between these three pruning methods in CC4.5 and C4.5 as described in section 4." @default.
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- W138371386 date "2005-07-09" @default.
- W138371386 modified "2023-09-24" @default.
- W138371386 title "Use of expert knowledge for decision tree pruning" @default.
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