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A Benefit-Cost Based Method for Cost-Sensitive Decision Trees

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1 Author(s)
Xingyi Liu ; Qinzhou Univ., Qinzhou, China

Cost-sensitive learning is popular during the process of classification. A fundamental issue in decision tree inductive learning is the attribute selection measure at each non-terminal node of the tree. However, existing literatures have not taken the trade-off between cost and benefit into account well. In this paper, we present a new strategy for attributes selection, which is a trade-off method between classification ability and cost-sensitive learning including misclassification costs and test costs with different units, for selecting splitting attributes in cost-sensitive decision trees induction. The experimental results show our method outperform the existed methods in terms of the decrease of misclassification cost.

Published in:

Intelligent Systems, 2009. GCIS '09. WRI Global Congress on  (Volume:3 )

Date of Conference:

19-21 May 2009