Greedy polynomial neural network for classification task in data mining

In this paper, a greedy polynomial neural network (GPNN) for the task of classification is proposed. Classification task is one of the most studied tasks of data mining. In solving classification task of data mining, the classical algorithm such as Polynomial Neural Network (PNN) takes large computation time because the network grows over the training period i.e. the partial descriptions (PDs) in each layer grows in successive generations. Unlike PNN this proposed work restricts the growth of partial descriptions to a single layer. A greedy technique is then used to select a subset of PDs those who can best map the input-output relation in general. Performance of this model is compared with the results obtained from PNN. Simulation result shows that the performance of GPNN is encouraging for harnessing its power in data mining area and also better in terms of processing time than the PNN model.