Histogram-Based Feature Selection for Binary Classification

This paper presents a novel method for feature selection in binary classification tasks based on histogram-based scoring. By leveraging the distribution differences between feature values associated with positive and negative classes, we generate a score to determine the most informative features. The method, called Histogram-Based Feature Selection (HBFS), has been tested against a variety of datasets and compared to the Fisher Score for performance assessment. Our findings indicate that HBFS either matches or outperforms Fisher Score in most datasets.