Particle Swarm Optimised polynomial neural network for classification: a multi-objective view Online publication date: Fri, 06-Feb-2009
by S. Dehuri, A. Ghosh, S-B. Cho
International Journal of Intelligent Defence Support Systems (IJIDSS), Vol. 1, No. 3, 2008
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Defence Support Systems (IJIDSS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com