Optimal Selection of Features Using Artificial Electric Field Algorithm for Classification

The high-dimensional features in the data may affect the performance of the classification model as all of them are not useful. The selection of relevant optimal features is a tedious task, especially the data in which the number of features is high. This paper proposed a new feature selection (FS) approach based on the artificial electric field algorithm (AEFA) called FSAEFA, to select the most suitable optimal features for classification. The AEFA is an efficient and effective population-based optimization technique inspired by the principle of Coulomb's law of electrostatic force (CLEF) to solve optimization problems. The proposed FSAEFA approach can search the most suited subset of features by attracting the worst individual features towards the best individual features by using the attraction of CLEF. The proposed FSAEFA approach has been evaluated and compared with some other FS approaches on ten publicly available benchmark datasets. The experimental result has been indicated that the performance of the proposed method is superior over the existing methods in most of the cases. The significance of the proposed FS approach along with their counter parts have been statistically measured and compared by using Friedman test and Holm procedure. It has been determined that the proposed FSAEFA approach is found to be more efficient than the existing FS approaches.