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

Authors: Swathi Jamjala Narayanan; Rajen B. Bhatt; Ilango Paramasivam; M. Khalid; B.K. Tripathy

Addresses: School of Computing Science and Engineering, VIT University, Vellore-632014, India ' Robert Bosch Research and Technology Cente, Pittsburgh, PA 15203, USA ' School of Computing Science and Engineering, VIT University, Vellore-632014, India ' Director Designate, NITMAS, Kolkata, India ' School of Computing Science and Engineering, VIT University, Vellore-632014, India

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.

Keywords: fuzzy c-means; FCM; grid partitioning; Gaussian membership function; gradient descent; gradient projection; fuzzy ID3; intelligent paradigms; fuzzy decision trees; neuro-fuzzy decision trees; neural networks; fuzzy logic; classification rules; classification accuracy; fuzzy rules.

DOI: 10.1504/IJAIP.2014.066983

International Journal of Advanced Intelligence Paradigms, 2014 Vol.6 No.4, pp.346 - 369

Received: 09 Jan 2014
Accepted: 26 Aug 2014
Published online: 24 Jan 2015 *

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