Yager @IEEE Trans Syst Man Cybern B 2004;34:1952–1963# introduced a continuous interval argument OWA ~C-OWA! operator, which extends the ordered weighted averaging ~OWA! operator, introduced by Yager @IEEE Trans Syst Man Cybern B 1988;18:183–190#, to the case in which the given argument is a continuous valued interval rather than a finite set of values. In this article, we utilize the C-OWA ope...

Prior weights are necessary for the application of ordered weighted averaging (OWA) operators, but obtaining them is expensive and contentious, which restricts operators. To address weighting issue, weight space used to “replace” conventional vector, operator comparison then extended a partial order on space. The results show that OWA can be as long clear, is, there no need take accurate values...

The ordered weighted averaging (OWA) operator was developed by Yager [IEEE Trans. Syst., Man, Cybernet. 18 (1998) 183]. Later, Yager and Filev [IEEE Trans. Syst., Man, Cybernet.––Part B 29 (1999) 141] introduced a more general class of OWA operators called the induced ordered weighted averaging (IOWA) operators, which take as their argument pairs, called OWA pairs, in which one component is use...

The problem of aggregating multiple numerical attributes to form overall measure is of considerable importance in many disciplines. The ordered weighted averaging (OWA) aggregation, introduced by Yager, uses the weights assigned to the ordered values rather than to the specific attributes. This allows one to model various aggregation preferences, preserving simultaneously the impartiality (neut...

Actual result of aggregation performed by an ordered weighted averaging (OWA) operator heavily depends upon the weighting vector used. A number of approaches for obtaining the associated weights have been suggested in the academic literature. In this paper, we present a method for determining the OWA weights when (1) the preferences of some subset of alternatives over other subset of alternativ...

In this article we extend the similarity classifier to cover also Ordered Weighted Averaging (OWA) operators. Earlier, similarity classifier was mainly used with generalized mean operator, but in this article we extend this aggregation process to cover more general OWA operators. With OWA operators we concentrate on linguistic quantifier guided aggregation where several different quantifiers ar...

In this paper a class of combinatorial optimization problems with uncertain costs is discussed. The uncertainty is modeled by specifying a discrete scenario set containing K distinct cost scenarios. The Ordered Weighted Averaging (OWA for short) aggregation operator is applied to choose a solution. The well-known criteria such as: the maximum, minimum, average, Hurwicz and median are special ca...

In this study we focus on improvement performance of a cue based Motor Imagery Brain Computer Interface (BCI). For this purpose, data fusion approach is used on results of different classifiers to make the best decision. At first step Distinction Sensitive Learning Vector Quantization method is used as a feature selection method to determine most informative frequencies in recorded signals and ...

Relevance Learning Vector Quantization (RLVQ) (introduced in [1]) is a variation of Learning Vector Quantization (LVQ) which allows a heuristic determination of relevance factors for the input dimensions. The method is based on Hebbian learning and defines weighting factors of the input dimensions which are automatically adapted to the specific problem. These relevance factors increase the over...

The aim of this paper is to introduce a unified model between the generalized ordered weighted averaging (GOWA) operator and the generalized probabilistic aggregation. We present the generalized probabilistic OWA (GPOWA) operator. It is a new aggregation operator that unifies the probability with the OWA operator considering the degree of importance that each concept has in the analysis. It inc...