A Flexible Classifier Based on Optimum Curve Fitting Approach

This study proposes a curve fitting approach for classification problems. The different classification data sets are utilized to test and evaluate the suggested method. For tested classification problems, the Gaussian curve fitting models are used. In the curve fitting stage, the number of curves equals the number of attributes in the related classification problem. For example, there are 4 attributes for iris dataset, thus four Gaussian curves are fitted for this problem. Then, output values of these fitted curves are calculated to average values, and this average value is rounded to the nearest integers. The same procedure is applied to the other dataset with having different number of features. In optimization stage, for each of classification application, the optimum values of constants of Gaussian function are determined by using genetic algorithm. For all used classification dataset, a part of the set is used during the optimization phase, and then the proposed model is validated with the remainder of the dataset. Furthermore, the optimal valuesof each of the attributes in tested classification application are determined by optimization algorithm. It is a valuable property of the proposed method that the accuracy of high classification can be achieved with a low number of reference data by the stage of determination of optimal feature set. Simulation results show that proposed classification approach with optimum values of constants and optimal feature set based on curve fitting has high accuracy rate. The proposed approach can be used for different classification problems.