This paper presents a feature ranking method adapted to fuzzy modelling with output from a continuous range. Existing feature selection/ranking techniques are mostly suitable for classification problems, where the range of the output is discrete. These techniques result in a ranking of the input feature (variables). Our approach exploits an arbitrary fuzzy clustering of the model output data. U...

This paper presents a feature ranking method adapted to fuzzy modelling with output from a continuous range. Existing feature selection/ranking techniques are mostly suitable for classification problems, where the range of the output is discrete. These techniques result in a ranking of the input feature (variables). Our approach exploits an arbitrary fuzzy clustering of the model output data. U...

Keywords: Fuzzy DEA Intuitionistic fuzzy DEA Optimistic and pessimistic performance Ranking methods Bank branch efficiency a b s t r a c t Intuitionistic fuzzy set (IFS) is an extension of fuzzy set and an approach to define a fuzzy set where available information is not sufficient to define an imprecise concept by means of a conventional fuzzy set. The existing fuzzy DEA (FDEA) models for meas...

In this paper we study some models for dealing with Fuzzy Integer Linear Programming problems which have a certain lack of precision of a vague nature in their formulation. We present methods to solve them with either fuzzy constraints, or fuzzy numbers in the objective function or fuzzy numbers defining the set of constraints. These methods are based on the representation theorem and on fuzzy ...

Many methods for ranking fuzzy numbers have been proposed. The existing methods for ranking generalized fuzzy numbers based on the optimistic index (α) gives different values for comparing the numbers by using different values of the optimistic index (α) and it is the shortcoming of this method. So we introduce a new defuzzification using a crisp number for ordering and comparing the fuzzy numb...

Ranking of fuzzy numbers play an important role in decision making, optimization, forecasting etc. Fuzzy numbers must be ranked before an action is taken by a decision maker. Cheng (Cheng, C. H. (1998). A new approach for ranking fuzzy numbers by distance method. Fuzzy Sets and Systems, 95, 307–317) pointed out that the proof of the statement ‘‘Ranking of generalized fuzzy numbers does not depe...

the importance as well as the diculty of the problem of ranking fuzzy numbers is pointed out. here we consider approaches to the ranking of fuzzy numbers based upon the idea of associating with a fuzzy number a scalar value, its signal/noise ratios, where the signal and the noise are dened as the middle-point and the spread of each a-cut of a fuzzy number, respectively. we use the value of a ...

The problem of Order Acceptance and Scheduling (OAS) is defined as a joint decision of which orders to accept for processing and how to schedule them. Any linear programming model representing real-world situation involves the parameters defined by the decision maker in an uncertain way or by means of language statement. Fuzzy data can be used to incorporate vagueness in the real-life situation...