A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Fast Branch & Bound Algorithms for Optimal Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
AIPR '04 Proceedings of the 33rd Applied Imagery Pattern Recognition Workshop
Feature selection with particle swarms
CIS'04 Proceedings of the First international conference on Computational and Information Science
Evolving RBF neural networks for pattern classification
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Dimensionality reduction including both feature selection and feature extraction techniques are useful for improving the performance of neural networks. In this paper, particle swarm optimization (PSO) algorithm was proposed for simultaneous feature extraction and feature selection. First PSO was used to simultaneous feature extraction and selection in conjunction with knearest- neighbor (k-NN) for individual fitness evaluation. With the derived feature set, PSO was then used to evolve RBF networks dynamically. Experimental results on four datasets show that RBF networks evolved with the derived feature set by our proposed algorithm have more simple architecture and stronger generalization ability with the similar classification performance when compared with the networks evolved with the full feature set.