K Thangavel, A Pethalakshmi - Applied soft computing, 2009 - Elsevier
A rough set theory is a new mathematical tool to deal with uncertainty and vagueness of decision system and it has been applied successfully in all the fields. It is used to identify the …
This study provides a comprehensive overview of the publications in Information Sciences (INS) from 1968 to 2016 inclusive, which encompasses the history of this journal from its …
Y Yao, B Yao - Information Sciences, 2012 - Elsevier
We propose a framework for the study of covering based rough set approximations. Three equivalent formulations of the classical rough sets are examined by using equivalence …
Q Hu, D Yu, J Liu, C Wu - Information sciences, 2008 - Elsevier
Feature subset selection is viewed as an important preprocessing step for pattern recognition, machine learning and data mining. Most of researches are focused on dealing …
HR Zhang, F Min - Knowledge-Based Systems, 2016 - Elsevier
Recommender systems attempt to guide users in decisions related to choosing items based on inferences about their personal opinions. Most existing systems implicitly assume the …
In this paper, we contribute to a recent and successful modelization of uncertainty, which the practitioner often encounters in the formulation of multicriteria group decision making …
Rough sets, a tool for data mining, deal with the vagueness and granularity in information systems. This paper studies covering-based rough sets from the topological view. We …
By introducing the new concepts of fuzzy β-covering and fuzzy β-neighborhood, we define two new types of fuzzy covering rough set models which can be regarded as bridges linking …
WZ Wu, Y Leung, JS Mi - IEEE transactions on knowledge and …, 2008 - ieeexplore.ieee.org
Granular computing and knowledge reduction are two basic issues in knowledge representation and data mining. Granular structure of concept lattices with application in …