An Efficient Gannet Optimization Algorithm for Feature Selection based on Sensitivity and Specificity

The selection of features is a crucial step in the analysis of high-dimensional data in machine learning and data mining. Gannet Optimization Algorithm (GOA) is a recently proposed metaheuristic algorithm that has not yet been investigated in terms of its capacity to solve feature selection problems. A new wrapper feature selection approach based on GOA is proposed to extract the best features. The GOA is a robust meta-heuristic algorithm that can deal with higher dimensions. A fitness function is used to account for the entropy of the sensitivity and specificity, as well as the accuracy of the classifier and the fraction of features selected. Additionally, four new algorithms are compared with the proposed algorithm in this paper. Based on the experimental results, fewer features can be obtained with a higher classification accuracy using the proposed algorithm.