Title: Particle Swarm Optimised polynomial neural network for classification: a multi-objective view
Authors: S. Dehuri, A. Ghosh, S-B. Cho
Addresses: Department of Information and Communication Technology, Fakir Mohan University, Vyasa Vihar, Balasore-756019, Orissa, India. ' Machine Intelligence Unit and Center for Soft Computing Research, Indian Statistical Institute Kolkata, 203 B.T. Road, Kolkata-108, India. ' Soft Computing Laboratory, Department of Computer Science, Yonsei University, 262 Seongsanro, Sudaemoon-gu, Seoul 120-749, Korea
Abstract: Classification using a Polynomial Neural Network (PNN) can be considered as a multi-objective problem rather than as a single objective one. Measures like predictive accuracy and architectural complexity used for evaluating PNN based classification can be thought of as two different conflicting objectives. Using these two metrics as the objectives of classification problem, this paper uses a Pareto based Particle Swarm Optimisation (PPSO) technique to find out a set of non-dominated solutions with less complex architecture and high predictive accuracy. The proposed method is used to train PNN through simultaneous optimisation of topological structure and weights. An extensive experimental study has been carried out to illustrate the importance and effectiveness of the proposed method.
Keywords: classification; polynomial neural networks; PSO; particle swarm optimisation; multi-objective problems; Pareto solutions; data mining.
International Journal of Intelligent Defence Support Systems, 2008 Vol.1 No.3, pp.225 - 253
Available online: 06 Feb 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article