Feature Selection (FS) methods based on fuzzy-rough set theory (FRFS) have employed the dependency function to guide the FS process with much success. More recently a method has been developed which uses fuzzy-entropy [9] to perform this task. Such use of fuzzy-entropy as an evaluation measure in fuzzy-rough feature selection can result in smaller subset sizes than those obtained through FRFS a...

Pawlak’s Rough set theory was originally proposed as a general mathematical tool for dealing with uncertainty in modeling imperfect knowledge. The purpose of this paper is to introduce the concept of multifuzzy rough sets by combining the multi-fuzzy set and rough set models. Some operations such as Complement, Union, Intersection etc. are defined for multi-fuzzy rough sets and De Morgan’s laws...

Structure of Rough Approximations Based on Molecular Lattices p. 69 Rough Approximations under Level Fuzzy Sets p. 78 Fuzzy-Rough Modus Ponens and Modus Tollens as a Basis for Approximate Reasoning p. 84 Logic and Rough Sets Rough Truth, Consequence, Consistency and Belief Revision p. 95 A Note on Ziarko's Variable Precision Rough Set Model and Nonmonotonic Reasoning p. 103 Fuzzy Reasoning Base...

Based on analysis of Pawlak’s rough set model in the view of single equivalence relation and the theory of fuzzy set, associated with multi-granulation rough set models proposed by Qian, two types of new rough set models are constructed, which are multi-granulation fuzzy rough sets. It follows the research on the properties of the lower and upper approximations of the new multi-granulation fuzz...

One of the most common tools for achieving optimization and adequate production process management is linear programming (LP) in various forms. However, there are specific cases application when implies several potential solutions instead one. Exactly such a problem solved this paper, which integrates Multi-Criteria Decision-Making (MCDM) model. First, was applied to optimize lying on line segm...

While desi ning radial basis function neural networks for classification, kzzy clustering is often used to position the hidden nodes in the input space. The main assumption of the clustering is that similar inputs produce similar out uts. In other words, it means that any two in ut patterns t o m the same cluster must be from the same cfass. Generalization is possible in the radial basis functi...

In this paper we extend the notion of rough convergence using the concept of ideals which automatically extends the earlier notions of rough convergence and rough statistical convergence. We define the set of rough ideal limit points and prove several results associated with this set.

The paper presents a new approach to fuzzy classification in the case of missing data. Rough-fuzzy sets are incorporated into logical type neuro-fuzzy structures and a rough-neuro-fuzzy classifier is derived. Theorems which allow determining the structure of the rough-neuro-fuzzy classifier are given. Several experiments illustrating the performance of the roughneuro-fuzzy classifier working in...

In this paper we introduce the notion of fuzzy interior ideals in ternary semigroups and investigated relations between fuzzy ideals and fuzzy interior ideals in terms of regularity. Here a characterization of fuzzy interior ideals is obtained in terms of fuzzy translation operator. The notions of rough and rough fuzzy interior ideals in a ternary semigroup are introduced. Relation between cong...