Mining fuzzy rules using an Artificial Immune System with fuzzy partition learning

Authors:
Edward Myk;Olgierd Unold
Affiliations:
The Institute of Computer Engineering, Control and Robotics, Wroclaw University of Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland;The Institute of Computer Engineering, Control and Robotics, Wroclaw University of Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland
Venue:
Applied Soft Computing
Year:
2011

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Abstract

The paper introduces accuracy boosting extension to a novel induction of fuzzy rules from raw data using Artificial Immune System methods. Accuracy boosting relies on fuzzy partition learning. The performance, in terms of classification accuracy, of the proposed approach was compared with traditional classifier schemes: C4.5, Naive Bayes, K^*, Meta END, JRip, and Hyper Pipes. The result accuracy of these methods are significantly lower than accuracy of fuzzy rules obtained by method presented in this study (paired t-test, P