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Pawlak rough set and neighborhood rough set are the two most common rough settheoretical models. Pawlak can use equivalence classes to represent knowledge,but it cannot process continuous data; neighborhood rough sets can processcontinuous data, but it loses the ability of using equivalence classes torepresent knowledge. To this end, this paper presents a granular-ball rough setbased on the granular-ball computing. The granular-ball rough set cansimultaneously represent Pawlak rough sets, and the neighborhood rough set, soas to realize the unified representation of the two. This makes thegranular-ball rough set not only can deal with continuous data, but also canuse equivalence classes for knowledge representation. In addition, we proposean implementation algorithms of granular-ball rough sets. The experimentalresults on benchmark datasets demonstrate that, due to the combination of therobustness and adaptability of the granular-ball computing, the learningaccuracy of the granular-ball rough set has been greatly improved compared withthe Pawlak rough set and the traditional neighborhood rough set. Thegranular-ball rough set also outperforms nine popular or the state-of-the-artfeature selection methods.

A Unified Granular-ball Learning Model of Pawlak Rough Set and Neighborhood Rough Set