Induction of fuzzy decision trees and its refinement using gradient projected-neuro-fuzzy decision tree

Abstract

Fuzzy decision tree (FDT) induction is a powerful methodology to extract human interpretable classification rules. Due to the greedy nature of FDT, there is a chance of FDT resulting in poor classification accuracy. To improve the accuracy of FDT, Bhatt and Gopal (2006) have proposed a back propagation strategy, where the interpretability of derived fuzzy rules is affected, as the certainty factor of the rules does not lie within the theoretical bounds of 0 and 1. To retain the human interpretability of fuzzy rules, and to make rules consistent with fuzzy set theory, we restrict the values of certainty factor to lie within theoretical bounds using the concept of gradient projection over neuro fuzzy decision tree and the model is named as Gradient Projected-Neuro-Fuzzy Decision Tree (GP-N-FDT). Here, the parameters of FDT developed using Fuzzy ID3 algorithm are fine tuned using GP-N-FDT strategy to improve the classification accuracy.