Feature Selection Based F-Score and ACO Algorithm in Support Vector Machine
- 1 January 2009
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in 2009 Second International Symposium on Knowledge Acquisition and Modeling
- Vol. 1, 19-23
- https://doi.org/10.1109/kam.2009.137
Abstract
This study proposes a new strategy combining with the SVM(support vector machine) classifier for features selection that retains sufficient information for classification purpose. Our proposed approach uses F-score models to optimize feature space by removing both irrelevant and redundant features. To improve classification accuracy, the parameters optimization of the penalty constant C and the bandwidth of the radial basis function (RBF) kernel ¿ is an important step in establishing an efficient and high-performance support vector machine (SVM) model. Aiming at optimizing the parameters of SVM, this paper also presents a grid based ant colony optimization (ACO) algorithm to choose parameters C and ¿ automatically for SVM instead of selecting parameters randomly by human's experience and traditional grid searching algorithm, so that the classification feature numbers can be reduced and the classification performance can be improved simultaneously. Some experimental results confirm the feasibility and efficiency of the approach.Keywords
This publication has 1 reference indexed in Scilit:
- Ant system: optimization by a colony of cooperating agentsIEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1996