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<p><b>Abstract</b>—Automatic generation of fuzzy rule base and membership functions from an input-output data set, for reliable construction of an adaptive fuzzy inference system, has become an important area of research interest. The present paper proposes a new robust, fast acting adaptive fuzzy pattern classification scheme, named influential rule search scheme (IRSS). In IRSS, rules which are most influential in contributing to the error produced by the adaptive fuzzy system are identified at the end of each epoch and subsequently modified for satisfactory performance. This fuzzy rule base adjustment scheme is accompanied by an output membership function adaptation scheme for fine tuning the fuzzy system architecture. This iterative method has shown a relatively high speed of convergence. Performance of the proposed IRSS is compared with other existing pattern classification schemes by implementing it for Fisher's iris data problem and Wisconsin breast cancer data problems.</p>
Pattern classification, adaptive fuzzy systems, fuzzy c-means clustering, Tuning of fuzzy rule base and output membership functions.

A. Rakshit and A. Chatterjee, "Influential Rule Search Scheme (IRSS)-A New Fuzzy Pattern Classifier," in IEEE Transactions on Knowledge & Data Engineering, vol. 16, no. , pp. 881-893, 2004.
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