Histogram-Based Feature Selection for Binary Classification

Year 2024, Volume: 1 Issue: 2, 63 - 70, 20.12.2024

Selman Delil , Melih Ağraz , Birol Kuyumcu

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.

Keywords

Machine Learning, Feature Selection, Histogram-Based Feature Selection, Fisher Score

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