Feature Subset Selection Based on Bio-Inspired Algorithms
Many feature subset selection algorithms have been proposed and discussed for years. However, the problem of finding the optimal feature subset from full data still remains to be a difficult problem. In this paper, we propose novel methods to find the relevant feature subset by using biologically-inspired algorithms such as Genetic Algorithm and Particle Swarm Optimization. We also propose a variant of the approach considering the significance of each feature. We verified the performance of the proposed methods by experiments with various real-world datasets. Our feature selection methods based on the biologically-inspired algorithms produced better performance than other methods in terms of the classification accuracy and the feature relevance. In particular, the modified method considering feature significance demonstrated even more improved performance.
genetic algorithm particle swarm optimization feature redundancy and relevance wrapper approach inductive learning algorithm