Dimensional reduction based on artificial bee colony for classification problems

Abstract

High dimensionality of data is a limiting factor to data processing in many fields. It causes ambiguousness in identifying significant factors for data analysis. Dimension reduction is needed to separate irrelevant data from the desired data. This research proposes a novel method for dimension reduction based on artificial bee colony (ABC). The method employs swarm intelligence based on bee foraging model in order to select features that allow us to generate subsets of dimensions from the original high-dimensional data while the resulting subsets satisfy the defined objective. Support vector machine (SVM) is used in this study as fitness evaluation of ABC in classification problems. To evaluate our method, we tested it with five datasets and compared it with other dimension reduction algorithms. The result of this study shows that using ABC and SVM is suitable for reducing the dimension of data. Moreover, this approach provides efficient classification with high accuracy. © 2012 Springer-Verlag.