In this paper, we propose a novel algorithm for wavelet feature extraction as input to a supervised Multi-Class Classifier to improve classification performance. In particular, to select the best wavelets coefficient features, we first compute the energy-based variance distribution from wavelets coefficients at different subbands as well as the entropy-based fuzzy measures associated with the t...

Human reasoning is characterized by a degree of fuzziness and uncertainty. In the present paper we develop a fuzzy model for a better description of the reasoning process and we use the fuzzy systems’ total possibilistic uncertainty as well as the classical Shannon’s entropy (properly modified for use in fuzzy environments) in measuring the individuals’ reasoning skills. Classroom experiments a...

In this paper, we investigate a new method to handle multiple attribute group decision making (MAGDM) problems based on combined ranking value under interval type-2 fuzzy environment, in which all the attribute values provided by experts take the form of interval type-2 fuzzy sets (IT2FSs). We first introduce some basic concepts and related operational laws on IT2FSs. Then, we put forward three...

In this paper, the utility of credibilistic critical values in crisp conversion of fuzzy data sets is considered. Conversion of this type becomes essential mainly when clustering of fuzzy data sets is carried out. In this paper performance of two popular clustering algorithms namely Fuzzy c–means and Fuzzy c–medoids algorithms are evaluated under credibilistic critical value crisp conversion is...

Parsimony is very important in system modeling as it is closely related to model interpretability. In this paper, a scheme for constructing accurate and parsimonious fuzzy models by generating distinguishable fuzzy sets is proposed, in which the distinguishability of input space partitioning is measured by a so-called “local” entropy. By maximizing this entropy measure the optimal number of mer...

William J. Tastle and et al. (Proc ISECON 2005, V22 (Columbus OH)) presented a new consensus measure for ranking sets of Likert scale data (ordinal data). In this paper we extend the consensus measure to the fuzzy sets. This measure called the strength of consensus is a modification of both the Shannon entropy, an equation common to the foundation of information theory, and the standard consens...