Evidence theory and VPRS model

The Rough Set Theory (RST) was proposed by Pawlak [4] as a new mathematical approach to deal with uncertain knowledge in expert systems. In 1991 Ziarko [11] proposed the Variable Precision Rough Set Model (VPRSM) as a certain extension of the rough set theory. VPRSM approach makes it possible to use a certain level of misclassi cation. The aim of this paper is to introduce belief and plausibility functions de ned by the {approximation regions. On the basis of the {approximation regions, the {basic probability assignment is de ned and the Dempster's combination rule for product of two decision tables is constructed. This entire approach is illustrated by examples

Extension of the fuzzy dominance-based rough set approach using ordered weighted average operators

In the article we rst review some known results on fuzzy versions of the dominance-based rough set approach (DRSA) where we expand the theory considering additional properties. Also, we apply Ordinal Weighted Average (OWA) operators in fuzzy DRSA. OWA operators have shown a lot of potential in handling outliers and noisy data in decision tables when it is combined with the indiscernibility-based rough set approach (IRSA).We examine theoretical properties of the proposed combination with fuzzy DRSA

A rough set approach for the discovery of classification rules in interval-valued information systems

A novel rough set approach is proposed in this paper to discover classification rules through a process of knowledge induction which selects optimal decision rules with a minimal set of features necessary and sufficient for classification of real-valued data. A rough set knowledge discovery framework is formulated for the analysis of interval-valued information systems converted from real-valued raw decision tables. The optimal feature selection method for information systems with interval-valued features obtains all classification rules hidden in a system through a knowledge induction process. Numerical examples are employed to substantiate the conceptual arguments

Rough sets theory and uncertainty into information system

This article is focused on rough sets approach to expression of uncertainty into information system. We assume that the data are presented in the decision table and that some attribute values are lost. At first the theoretical background is described and after that, computations on real-life data are presented. In computation we wok with uncertainty coming from missing attribute values

Class Association Rules Mining based Rough Set Method

This paper investigates the mining of class association rules with rough set approach. In data mining, an association occurs between two set of elements when one element set happen together with another. A class association rule set (CARs) is a subset of association rules with classes specified as their consequences. We present an efficient algorithm for mining the finest class rule set inspired form Apriori algorithm, where the support and confidence are computed based on the elementary set of lower approximation included in the property of rough set theory. Our proposed approach has been shown very effective, where the rough set approach for class association discovery is much simpler than the classic association method.Comment: 10 pages, 2 figure