Simplified Polynomial Neural Network for classification task in data mining
Bijan Bihari Misra, Birendra Biswal, Pritam Dash, Ganapati Panda · 2007
In solving classification task of data mining the traditional polynomial neural network (PNN) algorithm takes longer time while generating complex mathematical models. PNN algorithm takes the combinations two or three inputs to generates one partial description (PD) for the next layer. The output of the PDs becomes the input to the next layer. The number of PDs in each layer increases very fast, which consume lot of time for evaluation of the coefficients of the PDs, consume huge memory and increase complexity of the model. We propose simplified polynomial neural network (SPNN) for the task of classification. PDs for a single layer of the PNN model are developed. The outputs of these PDs along with the original inputs from the dataset are fed to a single perception model of artificial neural network (ANN) without any hidden layers. The ANN is trained with gradient descent method as well as with particle swarm optimization (PSO) technique. The results of both techniques for training are considered for the comparison of the performance. Simulation and result shows that the performance of SPNN is better than PNN model.