On Decision Support Under Risk by the WOWA Optimization

Ogryczak, Włodzimierz; Śliwiński, Tomasz

doi:10.1007/978-3-540-75256-1_68

Cited by 10 publications

(5 citation statements)

References 15 publications

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Order By: Relevance

“…The WOWA may be expressed with more direct formula where preferential (OWA) weights w i are applied to averages of the corresponding portions of ordered achievements (quantile intervals) according to the distribution defined by importance weights p i [18], [19]:…”

Section: Weighted Owa Enhancementmentioning

confidence: 99%

Reference Point Method with Importance Weighted Partial Achievements

Ogryczak

2008

JTIT

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The reference point method (RPM) is based on the so-called augmented max-min aggregation where the worst individual achievement maximization process is additionally regularized with the average achievement. In order to avoid inconsistencies caused by the regularization, we replace it with the ordered weighted average (OWA) which combines all the individual achievements allocating the largest weight to the worst achievement, the second largest weight to the second worst achievement, and so on. Further following the concept of the weighted OWA (WOWA) we incorporate the importance weighting of several achievements into the RPM. Such a WOWA RPM approach uses importance weights to affect achievement importance by rescaling accordingly its measure within the distribution of achievements rather than by straightforward rescaling of achievement values. The recent progress in optimization methods for ordered averages allows us to implement the WOWA RPM quite effectively as extension of the original constraints and criteria with simple linear inequalities.

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Section: Weighted Owa Enhancementmentioning

confidence: 99%

Reference Point Method with Importance Weighted Partial Achievements

Ogryczak

2008

JTIT

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“…The WOWA aggregation was used to order the Order Weighted Averaging (OWA) for finding the preferential weights and model various preferences with respect to the risk. The importance weights are also calculated for the reliability of the data values in the Dataset [14]. The advantage of using the WOWA is that it unifies the WA and the OWA taking into account the degree of importance in Multi Criteria decision Making [15].…”

Section: The Wowa Operatormentioning

confidence: 99%

Fuzzy Weighted Ordered Weighted Average-Gaussian Mixture Model for Feature Reduction

Charles¹,

Arockiam

2005

IJCT

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Feature reduction finds the optimal feature subset using machine learning techniques and evaluation criteria. Some of the irrelevant features are existed in the real-world datasets that should be removed by using the multi criterion decision approach. The relevant features are determined by using the WOWA criteria in fuzzy set. There are two important criteria are considered such as preferential weights and importance weights of features. These weights are used to find the irrelevant features and they are removed from the mixture. In this context, WOWA operator has the capability of assigning the preferential weights and important weights to the features. It helps to obtain the irrelevant, by selecting the relevant features using the weights in the feature reduction process. The objective of this paper is to propose a FWOWA approach helps to discard the irrelevant features by avoiding the overfitting and improve the accuracy of the cluster. The irrelevant features are determined by applying WOWA. By applying WOWA, the irrelevant features are examined and it is removed from the Gaussian Mixture using (RPEM).Â Â Â

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“…Beliakov [3] constructed monotone quadratic interpolating spline for WOWA operator. Ogryczak et al [18,19] used the WOWA operator to analyze the linear programming optimization models those are related to preference information. Llamazares [15] showed some behaviors of the WOWA operators.…”

Section: Introductionmentioning

confidence: 99%

Parametric WOWA operator and its application in dynamic decision making

Sang

Liu

Chen

2014

Journal of Intelligent &Amp; Fuzzy Systems

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We develop the parametric weighted ordered weighted average (WOWA) operator in dynamic decision problems represented by multi-criteria decision tree, which combines the regular increasing monotonic (RIM) quantifiers with piecewise linear function. The parametric WOWA operator with piecewise linear function can be used to represent various preferences of the decision makers in the forms of risk pessimistic, risk neutral and risk optimistic, respectively. The expected utility theory becomes a special case of it with risk neutral attitude. Meanwhile, some properties of the parametric WOWA operator with piecewise linear function are provided and the advantages of it in solving dynamic decision making problems are summarized. Different attitudes of the decision makers can be implemented by changing the values of parameter in parametric WOWA operators under different application contexts. Finally, an example is introduced to illustrate our theoretical results with decision tree for introducing and pricing new product.

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On Decision Support Under Risk by the WOWA Optimization

Cited by 10 publications

References 15 publications

Reference Point Method with Importance Weighted Partial Achievements

Reference Point Method with Importance Weighted Partial Achievements

Fuzzy Weighted Ordered Weighted Average-Gaussian Mixture Model for Feature Reduction

Parametric WOWA operator and its application in dynamic decision making

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