Agent-Based RBF Network Classifier with Feature Selection in a Kernel Space. Issue 1 (2nd January 2016) - Czarnowski, Ireneusz; Jędrzejowicz, Piotr
ABSTRACT: The article addresses the problem of feature selection in a kernel space and proposes the approach to selecting the most informative features for classification carried out using the RBF neural classifier. In the article, class-dependent and cluster-dependent methods for feature selection are considered. The process of feature selection is supported by the rotation-based ensemble technique. The feature selection problem is viewed as an optimization task solved by the agent-based population learning algorithm applied at the RBFN's initialization and training stage. The proposed approach is validated experimentally, and the obtained results are compared with the results produced using other methods. Experiment results show that the proposed method of feature selection in a kernel space of the RBF neural networks can be considered as a useful approach to constructing high-quality RBFN-based classifiers.