Boosting Feature Selection Using Modified Grasshopper Algorithm: Emphasizing Social Interaction

Feature Selection is a way of improving machine learning models in terms of efficacy and accuracy. The process involves identifying the most relevant features within a dataset to improve the efficiency of the model. Traditionally, the approaches have had issues in selecting the most relevant features to the case in most instances with accuracy. This paper, therefore, looks to develop and evaluate a novel approach for feature selection based on the Grasshopper Algorithm (GH). The idea is to address some specific problems of feature selection tasks and assess its performance against traditional swarm intelligence techniques. In this regard, the modified GH has been comprehensively assessed against the traditional or common techniques like ABC, GA and PSO. The results which are obtained reveal that modified GH algorithm outperformed PSO, GA and ABC in all the feature selection tasks. It improved the accuracy to 92.01%, which is 10.84% higher than PSO, 25.12% higher than GA, and 12.92% higher than ABC. That means the GH algorithm performs very well in feature selection. Consequently, swarms of algorithms are rather competitive for the optimization performance of various machine learning applications. To this end, this paper reveals the pertinent knowledge of swarm intelligence in feature selection to researchers and readers.