Medical Decision Support Systems (MDSSs) are sophisticated, intelligent systems that can provide inference due to lack of information and uncertainty. In such systems, to model the uncertainty various soft computing methods such as Bayesian networks, rough sets, artificial neural networks, fuzzy logic, inductive logic programming and genetic algorithms and hybrid methods that formed from the co...

Rough set theory (RST) is a modern tool for dealing with uncertainty, granularity, and incompleteness of knowledge in information systems. One of the limitations of RST is its dependence on portioning the universe according to equivalence relation on the universe of objects in information systems. The purpose of this paper is to construct connections between generalized rough sets based on cove...

This work promotes a novel point of view in rough set applications: rough sets rule learning for ordinal prediction is based on rough graphical representation of the rules. Our approach tackles two barriers of rule learning. Unlike in typical rule learning, we construct ordinal prediction with a mathematical approach, rough sets, rather than purely rule quality measures. This construction resul...

Big data analysis includes many theories and methods for prediction system. Statistical methods such as Person’s correlation, Regression analysis and Rough Set Theory etc are being used for predicting facts. Also theory like collaboration filtering uses word’s filtering to predict and provide recommendations. We have studied all these methods and selected most appropriate method for student’s p...

Data used in machine learning applications is prone to contain both vague and incomplete information. Many authors have proposed to use fuzzy rough set theory in the development of new techniques tackling these characteristics. Fuzzy sets deal with vague data, while rough sets allow to model incomplete information. As such, the hybrid setting of the two paradigms is an ideal candidate tool to c...

Covering-based rough set theory is a useful tool to deal with inexact, uncertain or vague knowledge in information systems. Geometric lattice has widely used in diverse fields, especially search algorithm design which plays important role in covering reductions. In this paper, we construct four geometric lattice structures of covering-based rough sets through matroids, and compare their relatio...

-In order to solve the problem of the redundant information to distinguish in the risk decision-making, in this paper, the data mining algorithms based on Rough Sets is studied. And we know the risk decision-making is an important aspect in the management practice. In the risk decision process of a project decision-making, it is necessary to use the algorithm to discover valuable knowledge and ...

This paper summarizes various formulations of the standard rough set theory. It demonstrates how those formulations can be adopted to develop different generalized rough set theories. The relationships between rough set theory and other theories are discussed. 1 Formulations of Standard Rough Sets The theory of rough sets can be developed in at least two different manners, the constructive and ...

Recently, the rough set and fuzzy set theory have generated a great deal of interest among more and more researchers. Granular computing (GrC) is an emerging computing paradigm of information processing and an approach for knowledge representation and data mining. The purpose of granular computing is to seek for an approximation scheme which can effectively solve a complex problem at a certain ...