Evolutionary Teaching-Learning Based Modified Polynomial Classifier

Development of a new and robust classification method is yet a hot area of research. The polynomial neural network(PNN) is one such method. However, its model complexity and high computation time restrict its usage. This paper proposes an evolutionary teaching-learning base modified polynomial classifier(ETL-PC), a modified PNN model which initially starts with a polynomial neural network and then uses an artificial neural network to find the output. The model uses a binary encoded genetic algorithm to select a subset of features and partial descriptions(PDs) relevant for classification and uses teaching-learning based optimization method to determine the appropriate weights. The performance of ETL-PC is compared with the performance of few other classifiers using different metrics and it is observed that ETL-PC performs either better or as other methods in many cases and may be considered as a competitive model.