A Decision Tree Model Based on Preference Cost Sensitive

Jian-hua LUO, Yi-shan CHEN, Xin-hua ZHU


In view of the fact that the existing decision tree models have not considered the decision makers’ preference behaviors during the classification process, these models cannot predict the problems with obvious preference very well. Driven by demand, this paper puts forward a new decision tree algorithm—a preference cost sensitive decision tree (PCSDT). This algorithm introduces the preference degree and preference cost to decision tree. Then, we utilize the effective preference to construct a new attribute selection factor (ASF) and the class label distribution rule for a node. Finally, a preference sensitive decision tree with best preference degree is generated by adaptively adjusting the preference degree. Experiments have proved that the proposed method can guarantee better overall accuracy and performances than other traditional algorithms.


Decision tree, Preference cost matrix, Classification


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