Simultaneous Feature Selection and Support Vector Machine Optimization Using the Grasshopper Optimization Algorithm

Abstract

Support vector machine (SVM) is considered to be one of the most powerful learning algorithms and is used for a wide range of real-world applications. The efficiency of SVM algorithm and its performance mainly depends on the kernel type and its parameters. Furthermore, the feature subset selection that is used to train the SVM model is another important factor that has a major influence on it classification accuracy. The feature subset selection is a very important step in machine learning, specially when dealing with high-dimensional data sets. Most of the previous researches handled these important factors separately. In this paper, we propose a hybrid approach based on the Grasshopper optimisation algorithm (GOA), which is a recent algorithm inspired by the biological behavior shown in swarms of grasshoppers. The goal of the proposed approach is to optimize the parameters of the SVM model, and locate the best features subset simultaneously. Eighteen low- and high-dimensional benchmark data sets are used to evaluate the accuracy of the proposed approach. For verification, the proposed approach is compared with seven well-regarded algorithms. Furthermore, the proposed approach is compared with grid search, which is the most popular technique for tuning SVM parameters. The experimental results show that the proposed approach outperforms all of the other techniques in most of the data sets in terms of classification accuracy, while minimizing the number of selected features.