Accuracy vs. Interpretability of Fuzzy Rule-Based Classifiers: An Evolutionary Approach

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Accuracy vs. Interpretability of Fuzzy Rule-Based Classifiers: An Evolutionary Approach

Gorzałczany, Marian B.; Rudziński, Filip

Swarm and Evolutionary Computation

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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.

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Gorzałczany, Marian B. | Rudziński, Filip

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