Increasing accuracy of two-class pattern recognition with enhanced fuzzy functions

In building an approximate fuzzy classifier system, significant effort is laid on estimation and fine-tuning of fuzzy sets. However, in such systems little thought is given to the way in which membership functions are combined within fuzzy rules. In this paper, a robust method, improved fuzzy classifier functions (IFCF) design is proposed for two-class pattern recognition problems. A supervised hybrid improved fuzzy clustering for classification (IFC-C) algorithm is implemented for structure identification. IFC-C algorithm is based on a dual optimization method, which yields simultaneous estimates of the parameters of c-classification functions together with fuzzy c partitioning of dataset based on a distance measure. The merit of novel IFCF is that the information on natural grouping of data samples i.e., the membership values, are utilized as additional predictors of each fuzzy classifier function to improve accuracy of system model. Improved fuzzy classifier functions are approximated using statistical and soft computing approaches. A new semi-non-parametric inference mechanism is implemented for reasoning. The experimental results of the new modeling approach indicate that the new IFCF is a promising method for two-class pattern recognition problems.