Kalman particle swarm optimized polynomials for data classification

Data classification is an important area of data mining. Several well known techniques such as decision tree, neural network, etc. are available for this task. In this paper we propose a Kalman particle swarm optimized (KPSO) polynomial equation for classification for several well known data sets. Our proposed method is derived from some of the findings of the valuable information like number of terms, number and combination of features in each term, degree of the polynomial equation etc. of our earlier work on data classification using polynomial neural network. The KPSO optimizes these polynomial equations with a faster convergence speed unlike PSO. The polynomial equation that gives the best performance is considered as the model for classification. Our simulation result shows that the proposed approach is able to give competitive classification accuracy compared to PNN in many datasets.