A contrast based feature selection algorithm for high-dimensional datasets in machine learning

Feature selection plays a pivotal role in enhancing machine learning models by identifying relevant features and eliminating redundancies. However, existing methods often face challenges with high computational costs and inefficiencies, particularly when applied to large-scale, high-dimensional datasets. To address these issues, we propose ContrastFS, a novel contrast-based feature selection method that evaluates feature importance by analyzing discrepancies in feature distributions across different classes. By leveraging dimensionless surrogate of class-wise feature statistics, ContrastFS enables efficient assessment of both feature relevance and redundancy. Comprehensive experiments on diverse benchmark datasets demonstrate that ContrastFS achieves computational efficiency that is several orders of magnitude higher than state-of-the-art methods while maintaining competitive accuracy. Furthermore, it effectively reduces feature redundancy, enhancing both model interpretability and performance. With its efficiency, scalability, and robustness, ContrastFS offers a powerful solution for feature selection in high-dimensional datasets, making it particularly suited for large-scale artificial intelligence applications where speed and accuracy are critical.