An Adaptive T-S type Rough-Fuzzy Inference System (ARFIS) for Pattern Classification
Faculty of Computing, Health and Science
School of Computer and Security Science
The Rough-Fuzzy hybridization scheme has become of research interest in pattern classification over the past decade. The present paper proposes a new Adaptive T-S type rough-fuzzy inference system (ARFIS) for pattern classification. Rough set theory is utilized to reduce the number of attributes and also to obtain a minimal set of decision rules based on input-output data sets. A T-S type fuzzy inference system is constructed by the automatic generation of membership functions and rules by the fuzzy c-means clustering algorithm and rough set theory, respectively. The generated T-S type rough-fuzzy inference system is adjusted by the least-squares fit and a conjugate gradient descent algorithm towards better performance with a validity checking for the minimal set of rules. The proposed ARFIS is able to reduce the number of rules which increases exponentially when more input variables are involved and also to assess the validity of the minimized decision rules. The performance of the proposed ARFIS is compared with other existing pattern classification schemes using Fisher's Iris and Wisconsin breast cancer data sets and shown to be very competitive.