A hybrid approach of differential evolution and artificial bee colony for feature selection

In this study, a hybrid method which combines Artificial Bee Colony Optimization Technique with Differential Evolution Algorithm is proposed for feature selection problem of classification tasks. The developed hybrid method was experimented on fifteen datasets from the UCI Repository which are commonly used in classification problems. The proposed hybrid feature selection method was also compared with the three most popular feature selection techniques that are Information Gain, ChiSquare and Correlation Feature Selection to evaluate its performance. The aim of this study is to reduce the number of features to be used during the classification process to improve run-time performance and accuracy of the classifier. The experimental results of this study showed that our developed hybrid method was able to select good features for classification tasks.