OWA Operators in Regression Problems

OWA operators in linear regression and detection of outliers

OWA Operators in Decision Making∗

In 1988 Ronald R. Yager [14] introduced a new aggregation technique based on the ordered weighted averaging (OWA) operators. The goal of this paper is to present a short survey of of OWA operators and illustrate their applicability by a real-life example.

OWA Operators in Generalized Distances

Different types of aggregation operators such as the ordered weighted quasi-arithmetic mean (Quasi-OWA) operator and the normalized Hamming distance are studied. We introduce the use of the OWA operator in generalized distances such as the quasiarithmetic distance. We will call these new distance aggregation the ordered weighted quasi-arithmetic distance (Quasi-OWAD) operator. We develop a gene...

Axiomatic generalizations of OWA operators

Axiomatic generalizations of OWA operators are introduces and discussed. First, OMA operators based on comonotone modularity are recalled. Then, several kinds of comonotone pseudoadditivity based OWA generalizations are characterized and exemplified. Some of already known OWA generalizations are thus seen from new points of view. In several cases an integral representation of generalized OWA op...

RLVQ determination using OWA operators

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...