A novel condensed polynomial neural network using particle swarm optimization (PSO) technique is proposed for the task of classification in this paper. In solving classification task classical algorithms such as polynomial neural network (PNN) and its variants need more computational time as the partial descriptions (PDs) grow over the training period layer-by-layer and make the network very complex. Unlike PNN the proposed network needs to generate the partial description for a single layer. The discrete PSO (DPSO) is used to select a relevant set of PDs as well as features with a hope to get better accuracy, which are in turn fed to the output neuron. The weights associated with the links from hidden to output neuron is optimized by PSO for continuous domain (CPSO). Performance of this model is compared with the results obtained from PNN. Simulation result shows that the performance of this model both in processing time and accuracy, is encouraging for harnessing its power in domain with large and complex data particularly in data mining area.
Bibliographical noteFunding Information:
Dr. S. Dehuri would like to thank the Department of Science and Technology, Govt. of India vide letter number SR/BY/E-07/2007 dated 03-01-2008 for financial support under the BOYSCAST fellowship 2007–2008 and Prof. S.-B. Cho, Department of Computer Science, Yonsei University, Seoul, Korea for providing the Soft Computing Laboratory facilities.
All Science Journal Classification (ASJC) codes