A novel framework of fuzzy oblique decision tree construction for pattern classification

Abstract

In this paper, some significant efforts on fuzzy oblique decision tree (FODT) have been done to improve classification accuracy and decrease tree size. Firstly, to eliminate data redundancy and improve classification efficiency, a forward greedy fast feature selection algorithm based on neighborhood rough set (NRS_FS_FAST) is introduced. Then, a new fuzzy rule generation algorithm (FRGA) is proposed to generate fuzzy rules. These fuzzy rules are used to construct leaf nodes for each class in each layer of the FODT. Different from the traditional axis-parallel decision trees and oblique decision trees, the FODT takes dynamic mining fuzzy rules as decision functions. Moreover, the parameter δ, which can control the size of the tree, is optimized by genetic algorithm. Finally, a series of comparative experiments are carried out with five traditional decision trees (C4.5, Best First Tree (BFT), amulti-class alternating decision tree (LAD), Simple Cart (SC), Naive Bayes Tree (NBT)), and recently proposed decision trees (FRDT, HHCART, and FMMDT-HB) on UCI machine learning datasets. The experimental results demonstrate that the FODT exhibits better performance on classification accuracy and tree size than the chosen benchmarks.