A robust binary secretary bird optimization method for high-dimensional data classification

Feature selection is a key step in machine learning-based decision systems, especially in medical and biomedical applications, where datasets often contain a large number of features that can negatively affect both accuracy and interpretability. In this study, we introduce the binary secretary bird optimization algorithm (B-SBOA), a binary version of the secretary bird optimization algorithm specifically developed for feature selection tasks. The proposed approach translates the hunting and escape behaviors of the secretary bird into effective binary search strategies, allowing a well-balanced trade-off between exploration and exploitation. B-SBOA was tested on twenty-five benchmark datasets from the UCI repository and compared with nine well-known binary metaheuristic algorithms, including PSO, GWO, MPA, HBO, SMA, SFOA, DOA, SCA, and MSO. The experimental results show that B-SBOA consistently delivers performance that is either superior to or competitive with existing methods across F-score, precision, recall, and other standard metrics. B-SBOA was evaluated on 25 benchmark datasets from the UCI repository and compared with nine well-known binary metaheuristic algorithms. The results show that B-SBOA achieves superior performance, with average improvements reaching 3-8% in F-score and 2-6% in precision and recall compared to competing methods. In several high-dimensional datasets such as Arrhythmia and Hillvalley, the proposed method achieved the highest classification accuracy while reducing the number of selected features.