Binary Banyan tree growth optimization: A practical approach to high-dimensional feature selection

High-dimensional feature spaces in Scientific and Technical Service Resources (STSR) classification present significant challenges, including increased computational costs and diminished accuracy. Identifying an optimal subset of features from raw text vectors is thus critical for effective data classification. This paper introduces a novel metaheuristic algorithm called Binary Banyan Tree Growth Optimization (BBTGO), specifically designed for high-dimensional feature selection (FS). Inspired by the unique growth patterns of the banyan tree, BBTGO leverages a combination of innovative Boolean vectors, including rooting, multi-trunk, and adjustment operator, along with a perturbation phase to enhance the search efficiency and reduce feature dimensionality. These operators enhance the search for promising regions and reduce features by utilizing the optimal solutions clustered within subgroups. Furthermore, BBTGO incorporates a dynamic adjustment mechanism that periodically activates different growth operators to meet the search demands of high-dimensional space. We rigorously evaluate the exploration and exploitation capabilities of BBTGO through comprehensive statistical analyses of various performance metrics. The proposed method demonstrates superior results on 12 high-dimensional benchmark datasets and is successfully applied to feature selection in STSR text classification tasks. Experimental results show that BBTGO significantly outperforms existing methods in terms of classification accuracy, selected features, convergence speed, and processing time. These results underscore the potential of BBTGO as a robust and versatile solution for high-dimensional FS, with broad applicability to real-world classification challenges.