Feature Selection via Fuzzy Rough Set Theory for Robust Classification: a Review and Comparative study

Abstract

Despite a variety of powerful classifiers available in machine learning today, most of them struggle with processing large-scale real-world datasets. Usually, these datasets contain irrelevant and redundant information that can negatively affect the model’s performance. To overcome this, feature selection has become a commonly used strategy to improve model performance by reducing dataset size while retaining essential information. Some feature selection techniques tend to require more information than what is provided in the given dataset, making them impractical in some cases. Alternatively, completely data-driven methods may lose critical information, as they can mistake vagueness or imprecision in the dataset for irrelevant or redundant features. Fuzzy-rough set theory offers a robust paradigm for tackling uncertainties, having been utilised across various domains, with feature selection being one of its most prominent applications. This paper presents an extensive review of feature selection methodologies grounded in fuzzy-rough set theory, accompanied by an empirical evaluation of multiple techniques to evaluate their effectiveness.