Comparative Analysis of Feature Selection Techniques with Metaheuristic Grasshopper Optimization Algorithm

Feature selection (FS) is a critical step used to identify the most relevant and informative features from a given dataset. Feature selection plays a crucial role in dimensionality reduction, improving the used model’s performance, enhancing the model’s interpretability, and reducing computational complexity. In this study, we conducted a comparative analysis of different feature selection approaches three of them are considered traditional approaches, namely Recursive Feature Elimination (RFE), Mutual Information, K-Best, and one is the heuristic approach which is the Grasshopper Optimization Algorithm (GOA). To evaluate the performance of these approaches, we applied them to classification and regression problems and we applied them in three distinct datasets: Zillow Home Value Prediction, Breast Cancer Wisconsin, and Adult Income. The results of this study indicate that the performance of feature selection methods varies depending on the considered dataset. We observed varying levels of effectiveness across the used datasets. For the Zillow dataset, the grasshopper optimization algorithm yielded the best performance, with a Mean Absolute Error (MAE) of 6.666. However, for the Breast Cancer dataset, the grasshopper optimization algorithm again emerged as the top-performing method, achieving an accuracy of 99.122%. While, for the Adult Income dataset, Mutual Information exhibited the best performance, achieving an accuracy of 86.150%. These findings highlight the importance of considering the complexities and characteristics of the dataset when choosing the feature selection method. Therefore, selecting an appropriate feature selection method may require applying different approaches for optimal feature selection and subsequent model performance.