IMOABC: An efficient multi-objective filter–wrapper hybrid approach for high-dimensional feature selection

作者：Jiahao Li, Tao Luo, Baitao Zhang, Min Chen, Jie Zhou · 发表于：Journal of King Saud University - Computer and Information Sciences · 年份：2024 · DOI：10.1016/j.jksuci.2024.102205 · 被引用次数：18 · 研究领域：Advanced Multi-Objective Optimization Algorithms、Metaheuristic Optimization Algorithms Research、Evolutionary Algorithms and Applications

With the development of data science, the challenge of high-dimensional data has become increasingly prevalent. High-dimensional data contains a significant amount of redundant information, which can adversely affect the performance and effectiveness of machine learning algorithms. Therefore, it is necessary to select the most relevant features from the raw data and perform feature selection on high-dimensional data. In this paper, a novel filter–wrapper feature selection method based on an improved multi-objective artificial bee colony algorithm (IMOABC) is proposed to address the feature selection problem in high-dimensional data. This method simultaneously considers three objectives: feature error rate, feature subset ratio, and distance, effectively improving the efficiency of obtaining the optimal feature subset on high-dimensional data. Additionally, a novel Fisher Score-based initialization strategy is introduced, significantly enhancing the quality of solutions. Furthermore, a new dynamic adaptive strategy is designed, effectively improving the algorithm’s convergence speed and enhancing its global search capability. Comparative experimental results on microarray cancer datasets demonstrate that the proposed method significantly improves classification accuracy and effectively reduces the size of the feature subset when compared to various traditional and state-of-the-art multi-objective feature selection algorithms. IMOABC improves the classification accuracy by 2.27...