By Topic

Improving Generalization of Neural Networks Using MLP Discriminant Based on Multiple Classifiers Failures

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
F. Siraj ; Coll. of Arts & Sci., Univ. Utara Malaysia, Sintok, Malaysia ; W. R. Sheik Osman

Multiple classifier systems or ensemble is an idea that is relevant both to neural computing and to machine learning community. Different MCSs can be designed for creating classifier ensembles with different combination functions. However, the best MCS can only be determined by performance evaluation. In this study, MCS is used to construct discriminant set that was used to discriminate the difficult to learn from the easy to learn patterns. Hence, this study explores several potentially productive ways in which an appropriate discriminant set or failure treatment might be developed based on the selection of the two failure cases: training failures and test failures. The experiments presented in this paper illustrate the application of discrimination techniques using multilayer perceptron (MLP) discriminants to neural networks trained to solve supervised learning task such as the Launch Interceptor Condition 1 problem. The experimental results reveal that directed splitting using an MLP discriminant is an important strategy in improving generalization of the networks.

Published in:

2010 Second International Conference on Computational Intelligence, Modelling and Simulation

Date of Conference:

28-30 Sept. 2010