Splitting attribute selection method based on cost performance
基于性价比的分裂属性选择方法

Cost-sensitive decision trees usually concern the discussion of the test cost and misclassification cost. During the classification process, splitting attribute selection is the most important. The paper analyzed the disadvantages and the advantages of the existing methods and proposed a novel method that combined the information ratio in information theory with the cost including the test cost and the misclassification cost to select the split attributes. The experimental results show that this method outperforms significantly the existing methods.