Rough sets, developed by Pawlak [6], are an important tool to describe a situation of incomplete or partially unknown information. One of the algebraic models deals with the pair of the upper and the lower approximation. Although usually the tolerance or the equivalence relation is taken into account when considering a rough set, here we rather concentrate on the model with the pair of two defi...

“Our claim is that knowledge is deep–seated in the classificatory abilities of human beings and other species. For example, knowledge about the environment is primarily manifested as an ability to classify a variety of situations from the point of view of survival in the real world : : : Classification on more abstract levels, seems to be a key issue in reasoning, learning and decision making, ...

Bipolar neutrosophic set theory and rough neutrosophic set theory are emerging as powerful tool for dealing with uncertainty, and indeterminate, incomlete, and inprecise information. In the present study we develop a hybrid structure called “rough bipoar neutrsophic set”. In the study, we define rough bipoar neutrsophic set and define union, complement, intersection and containment of rough bip...

In this paper, we propose a new rough set classifier induced from partially uncertain decision system. The proposed classifier aims at simplifying the uncertain decision system and generating more significant belief decision rules for classification process. The uncertainty is reperesented by the belief functions and exists only in the decision attribute and not in condition attribute values.

in this paper, we present a revised similarity measure based onchen-and-chen's similarity measure for fuzzy risk analysis. the revisedsimilarity measure uses the corrected formulae to calculate the centre ofgravity points, therefore it is more  effective than the chen-and-chen'smethod. the revised similarity measure can overcome the drawbacks of theexisting methods. we have also proposed a new ...

Fault diagnosis on a gear box is a difficult problem due to the non-stationary type of vibration signals it generates. Usually, one method of fault diagnosis can only inspect one corresponding fault category. Vibration based condition monitoring using machine learning methods is gaining momentum. In this paper, rough sets theory, is used to diagnose the fault gears in a gear box. Through the an...

A fuzzy set can be represented by a family of crisp sets using its α-level sets, whereas a rough set can be represented by three crisp sets. Based on such representations, this paper examines some fundamental issues involved in the combination of rough-set and fuzzy-set models. The rough-fuzzy-set and fuzzy-rough-set models are analyzed, with emphasis on their structures in terms of crisp sets....

Soft set theory is a newly emerging tool to deal with uncertain problems. Based on soft sets, soft rough approximation operators are introduced, and soft rough sets are defined by using soft rough approximation operators. Soft rough sets, which could provide a better approximation than rough sets do, can be seen as a generalized rough set model. This paper is devoted to investigating soft rough...