Rough Set Theory: Past, Present, and Future - A Comprehensive Review

Rough Set Theory (RST) has emerged as a powerful mathematical tool for dealing with vagueness and uncertainty in data analysis. Since its inception, RST has evolved through numerous extensions and hybrid models that aim to enhance its capacity in complex decision-making environments. This review presents a comprehensive overview of the evolution of RST—from its classical foundations to modern developments such as fuzzy rough sets, probabilistic rough sets, covering-based models, and information-theoretic variants. The study critically examines the inherent limitations of traditional RST, explores its integration with contemporary computational frameworks, and identifies open challenges and promising research directions for future advancement.