An improved Red-billed blue magpie feature selection algorithm for medical data processing

Feature selection is a crucial preprocessing step in the fields of machine learning, data mining and pattern recognition. In medical data analysis, the large number and complexity of features are often accompanied by redundant or irrelevant features, which not only increase the computational burden, but also may lead to model overfitting, which in turn affects its generalization ability. To address this problem, this paper proposes an improved red-billed blue magpie algorithm (IRBMO), which is specifically optimized for the feature selection task, and significantly improves the performance and efficiency of the algorithm on medical data by introducing multiple innovative behavioral strategies. The core mechanisms of IRBMO include: elite search behavior, which improves global optimization by guiding the search to expand in more promising directions; collaborative hunting behavior, which quickly identifies key features and promotes collaborative optimization among feature subsets; and memory storage behavior, which leverages historically valid information to improve search efficiency and accuracy. To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. In addition, compared with nine existing feature selection methods, IRBMO demonstrates significant advantages in terms of fitness value. To further enhance the performance, this paper also constructs the V2IRBMO variant by combining the S-shaped and V-shaped transfer functions, which further enhances the robustness and generalization ability of the algorithm. Experiments demonstrate that IRBMO exhibits high efficiency, generality and excellent generalization ability in feature selection tasks. In addition, used in conjunction with the KNN classifier, IRBMO significantly improves the classification accuracy, with an average accuracy improvement of 43.89% on 12 medical datasets compared to the original Red-billed Blue Magpie algorithm. These results demonstrate the potential and wide applicability of IRBMO in feature selection for medical data.