Article history: Received 25 October 2011 Received in revised form 16 March 2012 Accepted 23 June 2012 Available online 2 July 2012

Granular computing deals with information representation in the form of a number of entities or information granules. Information granules are made up of a collection of entities, usually of numeric level, joined due to their similarity, functional adjacency, indistinguishability, coherence or alikeness. The granular computing is associated to sets concepts, such as fuzzy sets, rough sets, inte...

Recently, much attention has been given to the rough set models based on two universes. And many rough set models based on two universes have been developed from different points of view. In this paper, a novel model, that is, rough Atanassov's intuitionistic fuzzy sets model over two different universes, is firstly proposed from Atanassov's intuitionistic point of view. We study some important...

A fuzzy rough granular self-organizingmap (FRGSOM) involving a 3-dimensional linguistic vector and connection weights, defined in an unsupervised manner, is proposed for clustering patterns having overlapping regions. Each feature of a pattern is transformed into a 3-dimensional granular space using a π-membership function with centers and scaling factors corresponding to the linguistic terms l...

In this paper, we present a new fuzzy-rough nearest neighbour (FRNN) classification algorithm, as an alternative to Sarkar’s fuzzyrough ownership function (FRNN-O) approach. By contrast to the latter, our method uses the nearest neighbours to construct lower and upper approximations of decision classes, and classifies test instances based on their membership to these approximations. In the expe...

A new fuzzy-rough nearest neighbour (FRNN) classification algorithm is presented in this paper, as an alternative to Sarkar’s fuzzyrough ownership function (FRNN-O) approach. By contrast to the latter, our method uses the nearest neighbours to construct lower and upper approximations of decision classes, and classifies test instances based on their membership to these approximations. In the exp...

1. INTRODUCTION: Set theory as a branch of human endeavour was developed by the efforts of many mathematicians [ Kam ]. Such a theory found many applications in science, technology and other fields. In an effort to capture uncertainity in human reasoning, Zadeh formulated and studied the theory of fuzzy sets. The theory of fuzzy sets found applications in many branches of science and technology...

In this paper, the dual hesitant fuzzy rough set (DHFRS) is studied from the viewpoint of assessment deviations. Firstly, according to the relationship between intuitionistic fuzzy set and vague set, the DHFRS is transferred into a fuzzy set, where the membership of any given element to it has multi-grouped values. By the idea of bootstrap sampling, a group of four sets are generated to describ...