[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5391/IJFIS.2014.14.1.1

On a Novel Way of Processing Data that Uses Fuzzy Sets for Later Use in Rule-Based Regression and Pattern Classification

Mendel, Jerry M. (Signal and Image Processing Institute, Ming Hsieh Department of Electrical Engineering, University of Southern California)

Publication Information

International Journal of Fuzzy Logic and Intelligent Systems / v.14, no.1, 2014 , pp. 1-7 More about this Journal

Abstract

This paper presents a novel method for simultaneously and automatically choosing the nonlinear structures of regressors or discriminant functions, as well as the number of terms to include in a rule-based regression model or pattern classifier. Variables are first partitioned into subsets each of which has a linguistic term (called a causal condition) associated with it; fuzzy sets are used to model the terms. Candidate interconnections (causal combinations) of either a term or its complement are formed, where the connecting word is AND which is modeled using the minimum operation. The data establishes which of the candidate causal combinations survive. A novel theoretical result leads to an exponential speedup in establishing this.

Keywords

Rule based regression; Pattern recognition; Fuzzy set;

Citations & Related Records

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