This paper reviews probabilistic approaches to rough sets in granulation, approximation, and rule induction. The Shannon entropy function is used to quantitatively characterize partitions of a universe. Both algebraic and probabilistic rough set approximations are studied. The probabilistic approximations are defined in a decision-theoretic framework. The problem of rule induction, a major appl...

Decision-theoretic rough set theory is quickly becoming a research direction in rough set theory, which is a general and typical probabilistic rough set model with respect to its threshold semantics and decision features. However, unlike the Pawlak rough set, the positive region, the boundary region and the negative region of a decision-theoretic rough set are not monotonic as the number of att...

A generalized multi-granulation rough set is proposed in this paper. In the new model, supporting characteristic function is defined and a parameter called information level is introduced to investigate that an object supports a concept precisely under majority granulations. Moreover, some important properties are discussed on the new multigranulation rough set. And it can be found that the pro...

The original rough set model was developed by Pawlak, which is mainly concerned with the approximation of sets described by a single binary relation on the universe. In the view of granular computing, the classical rough set theory is established through a single granulation. This paper extends Pawlak’s rough set model to amulti-granulation rough set model (MGRS), where the set approximations a...

Medical analysis has been approached by several machine learning methods for many years.Pattern recognition algorithms are capable of improving the quality of prediction, early diagnosis of diseases and disease classification. The classification complications in medical area are solved based on the outcome of medical analysis or report of medical treatment by the medical specialist. This resear...

This paper emphasizes studying on a new method to rough set theory by obtaining granules with maximal compatible classes as primitive ones in which any two objects are mutually compatible, proposes the upper and lower approximation computations to extend rough set models for further building multi-granulation rough set theory in incomplete information systems, discusses the properties and relat...

Granulation of information appears in many areas, such as machine learning, evidence theory, and data mining. Granular computing is the core research field in granulation of information. It is an effective tool for complex problem, massive data mining and fuzzy information processing. In the basis of principle of granularity, we aim to study the granular decomposing method in granules space bas...

Information granulation and concept approximation are some of the fundamental issues of granular computing. Granulation of a universe involves grouping of similar elements into granules to form coarse-grained views of the universe. Approximation of concepts, represented by subsets of the universe, deals with the descriptions of concepts using granules. In the context of rough set theory, this p...

This paper describes application of information granulation theory, on the design of rock engineering flowcharts. Firstly, an overall flowchart, based on information granulation theory has been highlighted. Information granulation theory, in crisp (non-fuzzy) or fuzzy format, can take into account engineering experiences (especially in fuzzy shape-incomplete information or superfluous), or engi...