Improving grasshopper optimization algorithm for hyperparameters estimation and feature selection in support vector regression

Abstract High-dimensionality is one of the major problems which affect the quality of the classification and prediction modeling. Support vector regression has been applied in several real problems. However, it is usually needed to tune manually the hyperparameters. In addition, SVR cannot perform feature selection. Nature-inspired algorithms have been used as a feature selection and as hyperparameters estimation procedure. In this paper, an improving grasshopper optimization algorithm (GOA) is proposed by adapting a new function of the main controlling parameter of GOA to enhance the exploration and exploitation capability of GOA. This improving is utilized to optimize the hyperparameters of the SVR with embedding the feature selection simultaneously. Experimental results, obtained by running on four datasets, show that our proposed algorithm performs better than cross-validation method, in terms of prediction, number of selected features, and running time. Besides, the experimental results of the proposed improving confirm the efficiency of the proposed algorithm in improving the prediction performance and computational time compared to other nature-inspired algorithms, which proves the ability of GOA in searching for the best hyperparameters values and selecting the most informative features for prediction tasks.