Accuracy vs. Interpretability of Fuzzy Rule-Based Classifiers: An Evolutionary Approach
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.
KeywordsMembership Function Fuzzy Rule Rule Base Input Attribute Possibility Distribution
Unable to display preview. Download preview PDF.
- 1.Alcala, R., Nojima, Y., Herrera, F., Ishibuchi, H.: Multiobjective genetic fuzzy rule selection of single granularity-based fuzzy classification rules and its interaction with the lateral tuning of membership functions. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 1–16 (2010)Google Scholar
- 4.Chen, J., Hou, Y., Xing, Z., Jia, L., Tong, Z.: A Multi-objective Genetic-based Method for Design Fuzzy Classification Systems. IJCSNS International Journal of Computer Science and Network Security 6(8A), 110–117 (2006)Google Scholar
- 8.Gorzałczany M.B.: Computational Intelligence Systems and Applications, Neuro-Fuzzy and Fuzzy Neural Synergisms. Physica-Verlag, Springer-Verlag Co., Heidelberg, New York (2002)Google Scholar
- 12.Ponce J., Karahoca A. (eds): Data Mining and Knowledge Discovery in Real Life Applications. IN-TECH, Vienna (2009)Google Scholar