Accuracy vs. interpretability of fuzzy rule-based classifiers: an evolutionary approach

Authors:
Marian B. Gorzałczany;Filip Rudziński
Affiliations:
Department of Electrical and Computer Engineering, Kielce University of Technology, Kielce, Poland;Department of Electrical and Computer Engineering, Kielce University of Technology, Kielce, Poland
Venue:
SIDE'12 Proceedings of the 2012 international conference on Swarm and Evolutionary Computation
Year:
2012

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Abstract

The paper presents a generalization of the Pittsburgh approach to learn fuzzy classification rules from data. The proposed approach allows us to obtain a fuzzy rule-based system with a predefined level of compromise between its accuracy and interpretability (transparency). The application of the proposed technique to design the fuzzy rule-based classifier for the well known benchmark data sets (Dermatology and Wine) available from the http://archive.ics.uci.edu/ml is presented. A comparative analysis with several alternative (fuzzy) rule-based classification techniques has also been carried out.