Sensitivity Degree Based Fuzzy SLIQ Decision Tree

The determination of membership function is fairly critical to fuzzy decision tree induction. Unfortunately, generally used heuristics show the pathological behaviour of the attribute tests at split nodes inclining to select a crisp partition. Hence, for generation of binary fuzzy tree, this paper proposes a method depending on the sensitivity degree of attributes to all kinds of classes 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 behaviour.Moreover, the sensitivity degree based algorithm outperforms G-FDT algorithm in respect to classification accuracy on several datasets from UCI machine learning repository.