Fuzzy SLIQ Decision Tree Based on Classification Sensitivity

Abstract

The determination of membership function is fairly critical to fuzzy decision tree induction. Unfortunately, generally used heuristics, such as SLIQ, show the pathological behavior of the attribute tests at split nodes inclining to select a crisp partition. Hence, for induction of binary fuzzy tree, this paper proposes a method depending on the sensitivity degree of attributes to all classes of training examples to determine the transition region of membership function. The method, properly using the pathological characteristic of common heuristics, overcomes drawbacks of G-FDT algorithm proposed by B. Chandra, and it well remedies defects brought on by the pathological behavior. Moreover, the sensitivity degree based algorithm outperforms G-FDT algorithm in respect to classification accuracy.