Feature subset selection plays an important role in data mining and machine learning applications. The main aim of feature subset selection is reducing dimensionality by removing irrelevant and redundant features and improving classification accuracy. This paper presents a supervised feature selection method called as Extended Fuzzy Absolute Information Measure (EFAIM) for different classifiers...

The aim of this work is to integrate the ideas of flexibility and uncertainty into Allen’s interval-based temporal logic [1], defining a new formalism (IA) which extends classical Interval Algebra (IA). We refer to Dubois, Fargier & Prade approach to Fuzzy Constraint Satisfaction Problem (FCSP ) [3], in which constraints are satisfied to a degree; this framework is based on Fuzzy Sets and Possi...

This paper aims to predict a breast cancer patient’s prognosis and to determine the most important prognostic factors by means of logistic regression (LR) as a conventional statistical method, multilayer backpropagation neural network (MLBPNN) as a neural network method, fuzzy K-nearest neighbour algorithm (FK-NN) as a fuzzy logic method, a fuzzy measurement based on the FK-NN and the leave-one...

In this paper, we propose novel methods to find the best relevant feature subset using fuzzy rough set-based attribute subset selection with biologically inspired algorithm search such as ant colony and particle swarm optimization and the principles of an evolutionary process. We then propose a hybrid fuzzy rough with K-nearest neighbor (KNN)-based classifier (FRNN) to classify the patterns in ...

Abstract The necessarily optimal solution is known as the most reasonable to linear programming problems with interval/fuzzy objective function coefficients. As it remains against certain fluctuations of coefficients, can be seen a robust solution. In this paper, we demonstrate that necessary optimality degree non-degenerate basic feasible obtained easily by utilizing tolerance approach. evalua...

One apparent limitation of the truth table approach is that it is designed for causal conditions that are simple presence/absence dichotomies (i.e., Boolean or "crisp" sets-chapter 3) or multichotomies (MVQCA--chapter 4). Many of the causal conditions that interest social scientists, however, vary by level or degree. For example, while it is clear that some countries are democracies and some ar...

In a graph G, a vertex dominates itself and its neighbors. A subset S of V is called a dominating set in G if every vertex in V-S is adjacent to at least one vertex in S. The minimum cardinality taken over all, the minimal double dominating set which is called Fuzzy Double Domination Number and which is denoted as ) (G fdd  . A set V S  is called a Triple dominating set of a graph G if every ...

Taking intuitionistic fuzzy sets as the structures of truth values, we propose the notions of intuitionistic fuzzy context-free grammars (IFCFGs, for short) and pushdown automata with final states (IFPDAs). Then we investigate algebraic characterization of intuitionistic fuzzy recognizable languages including decomposition form and representation theorem. By introducing the generalized subset c...

This study proposes a mathematical and morphological erosion calculation through defuzzification of the fuzzy set measure corresponding to the fuzzy integral by reducing detecting areas required for matching through areal labeling and filtering and applying the template matching uses edges to the detecting areas. It defines a λ-fuzzy set measure in application and all subsets of defuzzification...

To each fuzzy subgroup a fuzzy equivalence relation is associated and it is proved that a fuzzy subgroup is normal if and only if the operation of the group is compatible with its associated fuzzy equivalence relation. In the de-nition of fuzzy subgroup only the subset is fuzzy whilst the group operation remains crisp. In an opposite direction, another structure can be constructed maintaining c...