关键词: 分类, 决策树, 属性选择因子, 偏好敏感

Abstract: In view of the fact that decision makers will have preference to one certain result when in the face of several classification results, it proposes a Class Preference Sensitive Decision Tree algorithm based on correlation（CPSDT）. The algorithm introduces the concept of class-preference, the degree of class-preference and the preference cost matrix. To make up for the weakness that the correlations between non-class attributes are not considered when choosing the splitting attribute in the traditional decision tree constructing process, the algorithm uses the features pre-screened based on the correlation to exclude the redundant attributes before learning and reconstructs the attribute selection fator which is based on correlation. The experimental results show that this algorithm can reduce the size of the decision tree effectively. Further more, the algorithm can not only achieve the high precision prediction of preference class, but also can ensure the decision tree has good overall accuracy.

Key words: classification, decision tree, attribute selection factor, preference sensitive