The weighted OWA operator

Torra, Vicenç

doi:10.1002/(sici)1098-111x(199702)12:2<153::aid-int3>3.0.co;2-p

Cited by 543 publications

(329 citation statements)

References 22 publications

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“…This operator was originally introduced by Yager [29] to provide a means for aggregating scores with the satisfaction of multiple criteria, which unifies in one operator both conjunctive and disjunctive behaviour. Examples of alternative aggregation operators include the Weighted Mean and the Weighted OWA [23]. However, we have chosen the OWA because the orness-measure can be directly learned from the data.…”

Section: Methodsmentioning

confidence: 99%

Using Fuzzy Set Theory to Assess Country-of-Origin Effects on the Formation of Product Attitude

Brijs

Vanhoof

Brijs

et al. 2006

Modeling Decisions for Artificial Intelligence

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Abstract. Several researchers on country-of-origin (coo) have expressed their interest in knowing how consumers' emotional reactions toward coo-cues affect product attitude formation. This paper shows how Fuzzy Set Theory might serve as a useful approach to that problem. Data was gathered by means of selfadministered questionnaires. Technically, orness of OWA-operators enabled us to distinguish consumers expressing highly positive versus less positive emotions toward coo. It appeared that this variance in emotional estate goes together with a difference in aggregating product-attribute beliefs.

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Section: Methodsmentioning

confidence: 99%

Using Fuzzy Set Theory to Assess Country-of-Origin Effects on the Formation of Product Attitude

Brijs

Vanhoof

Brijs

et al. 2006

Modeling Decisions for Artificial Intelligence

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“…Of course, it could be possible to get a scenario where each attribute of the data stream has different importance. We can model this scenario using the WOWA operator [30], a linear combination of the WM and OWA. Another important drawback of WM is that a different weighting vector must be defined for each gradual itemset to mine.…”

Section: Definition 4 Let W Be a Weighting Vector Of Dimensionmentioning

confidence: 99%

Speed up gradual rule mining from stream data! A B-Tree and OWA-based approach

2009

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Gradual rules allow users to be provided with rules describing the ordering correlations among attributes. Such a rule is for instance given by the higher the salary and the lower the number of cars, the higher the number of tourist travels. Previously intensively used in fuzzy command systems, these rules were manually provided to the system. More recently, they have received attention from the data mining community and methods have been defined to automatically extract and maintain gradual rules from numerical databases. However, no method has been shown to be able to handle data streams, as no method is scalable enough to manage the high rate which stream data arrive at. In this paper, we thus propose an original approach to mine data streams for gradual rules. Our method is based on B-Trees and OWA (Ordered Weighted Aggregation) operator in order to speed up the process. B-Trees are used to store already-known gradual rules in order to maintain the knowledge over time, while OWA operators provide a fast way to discard non relevant data.

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“…The second paper, by Ogryczak, focuses on the reference point method (RPM) (Wierzbicki 1982) and develops an extension incorporating the importance weighting of several achievements following the concept of the WOWA operator (Torra 1997). This paper is followed by another one by Cabrerizo, Moreno, Pérez, and Herrera-Viedma in which the authors analyze different consensus approaches in fuzzy decision making.…”

Section: Structure Of the Special Issuementioning

confidence: 99%

“…A few new aggregation operators as extensions of the OWA and WOWA (Torra 1997) operator are also proposed.…”

Section: Structure Of the Special Issuementioning

confidence: 99%