Improved Bald Eagle Search Optimization Algorithm for Feature Selection in Classification

Feature selection serves as an effective way for decreasing the quantity of features within a dataset, which helps enhance the performance of classification in machine learning (ML). In this paper, we formulate a joint feature selection problem to reduce the number of selected features while improving the classification accuracy. We propose an improved bald eagle search (IBES) algorithm to solve the optimization problem. Specifically, the BES algorithm is enhanced by introducing the lévy flight mechanism in the selection phase to improve the global search capability of the algorithm. In addition, we adopt an adaptive weighting factor to balance the global and local search capabilities. Finally, a novel mutation mechanism incorporating Gaussian and differential mutation is proposed, which contributes to maintain the diversity of the population. Comparative experiments are conducted with six benchmark algorithms on eighteen typical datasets. The results analysis indicates that the proposed IBES algorithm can achieve higher classification accuracy with a small number of features.