Pattern classification using fuzzy relation and genetic algorithm

This paper aims to consider a soft computing approach to pattern classification using the basic tools of fuzzy relational calculus (FRC) and genetic algorithm (GA).

The paper introduces a new interpretation of multidimensional fuzzy implication (MFI) to represent the author's knowledge about the training data set. It also considers the notion of a fuzzy pattern vector (FPV) to handle the fuzzy information granules of the quantized pattern space and to represent a population of training patterns in the quantized pattern space. The construction of the pattern classifier is essentially based on the estimate of a fuzzy relation Ri between the antecedent clause and consequent clause of each one‐dimensional fuzzy implication. For the estimation of Ri floating point representation of GA is used. Thus, a set of fuzzy relations is formed from the new interpretation of MFI. This set of fuzzy relations is termed as the core of the pattern classifier. Once the classifier is constructed the non‐fuzzy features of a test pattern can be classified.

The performance of the proposed scheme is tested on synthetic data. Subsequently, the paper uses the proposed scheme for the vowel classification problem of an Indian language. In all these case studies the recognition score of the proposed method is very good. Finally, a benchmark of performance is established by considering Multilayer Perceptron (MLP), Support Vector Machine (SVM) and the proposed method. The Abalone, Hosse colic and Pima Indians data sets, obtained from UCL database repository are used for the said benchmark study. The benchmark study also establishes the superiority of the proposed method.

This new soft computing approach to pattern classification is based on a new interpretation of MFI and a novel notion of FPV. A set of fuzzy relations which is the core of the pattern classifier, is estimated using floating point GA and very effective classification of patterns under vague and imprecise environment is performed. This new approach to pattern classification avoids the curse of high dimensionality of feature vector. It can provide multiple classifications under overlapped classes.