Intelligent Feature Selection by Bacterial Foraging Algorithm and Information Theory

摘要: In this paper, an intelligent feature selection by bacterial foraging algorithm and mutual information is proposed. Feature important issue in the pattern classification problem. Particularly, case of classifying with a large number features or variables, accuracy computational time classifier can be improved using relevant subset to remove irrelevant, redundant, noisy data. The proposed method consists two parts: wrapper part optimization filter information. order select best achieve performance classifiers. Experimental results show that better for recognition problems other than conventional ones.