The investigation of the Bayesian rough set model

AbstractThe original Rough Set model is concerned primarily with algebraic properties of approximately defined sets. The Variable Precision Rough Set (VPRS) model extends the basic rough set theory to incorporate probabilistic information. The article presents a non-parametric modification of the VPRS model called the Bayesian Rough Set (BRS) model, where the set approximations are defined by using the prior probability as a reference. Mathematical properties of BRS are investigated. It is shown that the quality of BRS models can be evaluated using probabilistic gain function, which is suitable for identification and elimination of redundant attributes

The investigation of the Bayesian rough set model

AbstractThe original Rough Set model is concerned primarily with algebraic properties of approximately defined sets. The Variable Precision Rough Set (VPRS) model extends the basic rough set theory to incorporate probabilistic information. The article presents a non-parametric modification of the VPRS model called the Bayesian Rough Set (BRS) model, where the set approximations are defined by using the prior probability as a reference. Mathematical properties of BRS are investigated. It is shown that the quality of BRS models can be evaluated using probabilistic gain function, which is suitable for identification and elimination of redundant attributes

Probabilistic Rough Classification in Information Systems with Fuzzy Decision Attributes

Based on Pawlaks two way approximations on Rough Sets and using thresholds G.Ganesan et al in 2004 proposed a method of rough indexing an information system which ha s fuzzy decision attributes. The limitation of Pawlaks approximation is that it does not quantify the level of importance of the basic granules. Recently, Y.Y.Yao discussed Probabilistic Rough Set Model, which specified how basic granules could be quantif ied appropriately. In this paper, it is proposed to extend the work of G.Ganesan et al, taking into consideration the basic granule quantification mechanism of Probabilistic Set Model, thus generating more accurate rough indices for information systems wi th fuzzy decision attributes

Probabilistic Rough indices in Information Systems under Intuitionistic Fuzziness

The concept of classifying the records of the information system has been due to Two Way Approach [ ie, lower and upper approximations ] of Pawlak’s rough sets model. But the approximation does to take into consideration the degree of contribution of the basic categories. This deficiency was eliminated in early nineties by Ziarko who has proposed VPRS model and later on various efforts were made in defining a new Probabilistic Rough Set Model.In 2004, G.Ganesan et.al., have introduced the concept of classifying the records of the information system with fuzzy decision attributes using a threshold. Later, G. Ganesan extended this algorithm for any information system with intuitionistic fuzzydecision attributes.In this paper, we extended the work of G.Ganesan et.al., for the Probabilistic Rough Set Model to improve the efficiency of rough indices in the information system with intuitionistic fuzzy decision attributes

Analysing imperfect temporal information in GIS using the Triangular Model

Rough set and fuzzy set are two frequently used approaches for modelling and reasoning about imperfect time intervals. In this paper, we focus on imperfect time intervals that can be modelled by rough sets and use an innovative graphic model [i.e. the triangular model (TM)] to represent this kind of imperfect time intervals. This work shows that TM is potentially advantageous in visualizing and querying imperfect time intervals, and its analytical power can be better exploited when it is implemented in a computer application with graphical user interfaces and interactive functions. Moreover, a probabilistic framework is proposed to handle the uncertainty issues in temporal queries. We use a case study to illustrate how the unique insights gained by TM can assist a geographical information system for exploratory spatio-temporal analysis