Constructing a Fuzzy Rule Based Classification System Using Pattern Discovery

Abstract

Pattern discovery (PD), an algorithm which discovers patterns based on a statistical analysis of training data was used to generate rules for a fuzzy rule based classification system (FRBCS). Classification performance of the FRBCS when using rules discovered by the PD algorithm and of the PD algorithm functioning as a classifier applied to a number of linearly and non-linearly separable continuous-valued data sets was compared. The results indicate an increased performance for the FRBCS. The improvement comes through both an increase in correct classifications and a decrease in the error rate in the class distributions studied. The use of trapezoidal shaped input membership functions applied to the input data values allowed vagueness in the input events to be modelled and resulted in a more robust determination of the characteristics of the input data which in turn resulted in more accurate classification. In addition, the standard use of a co-occurrence based weighting of the rules by the FRBCS outperformed the weight-of-evidence based selection and use of input patterns by the PD classifier.