@article{article_1592601, title={Histogram-Based Feature Selection for Binary Classification}, journal={Transactions on Computer Science and Applications}, volume={1}, pages={63–70}, year={2024}, author={Delil, Selman and Ağraz, Melih and Kuyumcu, Birol}, keywords={Machine Learning, Feature Selection, Histogram-Based Feature Selection, Fisher Score}, abstract={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.}, number={2}, publisher={Galatasaray University}