An ensemble model for high dimensional feature selection based on binary arithmetic optimization algorithm

doi:10.1016/j.swevo.2026.102298

An ensemble model for high dimensional feature selection based on binary arithmetic optimization algorithm

2026-01-22

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DOI：10.1016/j.swevo.2026.102298

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Traditional feature selection algorithms often face performance degradation or even fail to execute when handling high-dimensional data with over 1000 features. Existing studies predominantly rely on the classical Particle Swarm Optimization (PSO). To investigate the applicability of multigoal strategies to other evolutionary algorithms, this paper extends the binary arithmetic optimization algorithm (BAOA) by incorporating a multigoal framework. The method begins by designing eight distinct yet interrelated goals based on four filter-based algorithms (PCC, CHI2, ReliefF, and NCA) to form goal groups. Furthermore, a sparse initialization method employing a roulette wheel selection strategy is introduced to reduce the number of initially selected features. The proposed Ensemble Binary Arithmetic Optimization Algorithm (EBAOA) integrates a multi-goal optimization mechanism into the original binary arithmetic optimization framework, achieving a significant reduction in error rate. Extensive experiments on 24 high-dimensional datasets demonstrate that EBAOA consistently selects the smallest feature subsets while maintaining the lowest error rates across multiple classifiers, including K-Nearest Neighbors, Support Vector Machine, and Random Forest. The results highlight the effectiveness of the multi-goal strategy, and sparse initialization in enhancing feature selection performance for high-dimensional data. The source code will be released upon acceptance at: https://github.com/zhongjiezhuang/EBAOA .

An ensemble model for high dimensional feature selection based on binary arithmetic optimization algorithm

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