  Pawlak rough set and neighborhood rough set are the two most common rough set
theoretical models. Pawlak can use equivalence classes to represent knowledge,
but it cannot process continuous data; neighborhood rough sets can process
continuous data, but it loses the ability of using equivalence classes to
represent knowledge. To this end, this paper presents a granular-ball rough set
based on the granular-ball computing. The granular-ball rough set can
simultaneously represent Pawlak rough sets, and the neighborhood rough set, so
as to realize the unified representation of the two. This makes the
granular-ball rough set not only can deal with continuous data, but also can
use equivalence classes for knowledge representation. In addition, we propose
an implementation algorithms of granular-ball rough sets. The experimental
results on benchmark datasets demonstrate that, due to the combination of the
robustness and adaptability of the granular-ball computing, the learning
accuracy of the granular-ball rough set has been greatly improved compared with
the Pawlak rough set and the traditional neighborhood rough set. The
granular-ball rough set also outperforms nine popular or the state-of-the-art
feature selection methods.
