Exploring the Intersection of Rough Set Theory and Machine Learning: A Review

The Rough Set (RS) theory has clinched more popularity in input dimensionality reduction and managing impreciseness in datasets. Rough set applications in artificial intelligence have grown many folds in recent times. This heightened interest led to the covering several research domains such as artificial intelligence development thinking, inductive reasoning, decision analysis, and machine learning. Further, the rough set theory concepts show a wide scope for applications in pattern recognition, expert systems, and knowledge discovery. This paper reviews rough set theory fundamentals and highlights several research directions and applications that utilize this theory. Additionally, it probes the rough set theory concepts applications in various machine learning techniques, such as clustering, feature selection, and rule induction.