A new soft likelihood function based on power ordered weighted average operator

2019 ◽  
Vol 34 (11) ◽  
pp. 2988-2999 ◽  
Author(s):  
Yutong Song ◽  
Yong Deng
Keyword(s):  
Likelihood Function ◽  
Weighted Average ◽  
Average Operator ◽  
Ordered Weighted Average

scholarly journals MAXIMAL ORNESS WEIGHTS WITH A FIXED VARIABILITY FOR OWA OPERATORS

2006 ◽  
Vol 14 (03) ◽  
pp. 271-276 ◽  
Author(s):  
T. MARCHANT
Keyword(s):  
Weighted Average ◽  
Average Operator ◽  
Owa Operators ◽  
Fixed Level ◽  
Ordered Weighted Average

When using the ordered weighted average operator, it can happen that one wants to optimize the variability (measured by the entropy (maximal) or by the variance (minimal)) of the weights while keeping the orness of this operator at a fixed level. This has been considered by several authors. Dually, there might be some contexts where one wishes to maximize the orness while guaranteeing some fixed variability. In this paper, we present two algorithms for finding such weights, when the variability is captured by the entropy and by the variance.

Extension of the MACBETH approach to elicit an ordered weighted average operator

2015 ◽  
Vol 3 (1-2) ◽  
pp. 65-105 ◽  
Author(s):  
Christophe Labreuche ◽  
Brice Mayag ◽  
Bertrand Duqueroie
Keyword(s):  
Weighted Average ◽  
Average Operator ◽  
Ordered Weighted Average

scholarly journals Parameterized Algorithms for Finding a Collective Set of Items

2020 ◽  
Vol 34 (02) ◽  
pp. 1838-1845
Author(s):  
Robert Bredereck ◽  
Piotr Faliszewski ◽  
Andrzej Kaczmarczyk ◽  
Dušan Knop ◽  
Rolf Niedermeier
Keyword(s):  
Parameterized Complexity ◽  
Weighted Average ◽  
Fixed Parameter Tractability ◽  
Parameterized Algorithms ◽  
Utility Value ◽  
Average Operator ◽  
Fixed Parameter ◽  
Ordered Weighted Average ◽  
Integer Weight

We extend the work of Skowron et al. (AIJ, 2016) by considering the parameterized complexity of the following problem. We are given a set of items and a set of agents, where each agent assigns an integer utility value to each item. The goal is to find a set of k items that these agents would collectively use. For each such collective set of items, each agent provides a score that can be described using an OWA (ordered weighted average) operator and we seek a set with the highest total score. We focus on the parameterization by the number of agents and we find numerous fixed-parameter tractability results (however, we also find some W[1]-hardness results). It turns out that most of our algorithms even apply to the setting where each agent has an integer weight.

Pythagorean Hesitant Fuzzy Hamacher Aggregation Operators in Multiple-Attribute Decision Making

2017 ◽  
Vol 28 (5) ◽  
pp. 759-776 ◽  
Author(s):  
Guiwu Wei ◽  
Mao Lu
Keyword(s):  
Decision Making ◽  
Weighted Average ◽  
Multiple Attribute Decision Making ◽  
Aggregation Operators ◽  
Average Operator ◽  
Multiple Attribute ◽  
Fuzzy Aggregation ◽  
Ordered Weighted Average ◽  
Algebraic Operations ◽  
Geometric Operator

Abstract The Hamacher product is a t-norm and the Hamacher sum is a t-conorm. They are good alternatives to the algebraic product and the algebraic sum, respectively. Nevertheless, it seems that most of the existing hesitant fuzzy aggregation operators are based on algebraic operations. In this paper, we utilize Hamacher operations to develop some Pythagorean hesitant fuzzy aggregation operators: Pythagorean hesitant fuzzy Hamacher weighted average operator, Pythagorean hesitant fuzzy Hamacher weighted geometric operator, Pythagorean hesitant fuzzy Hamacher ordered weighted average operator, Pythagorean hesitant fuzzy Hamacher ordered weighted geometric operator, Pythagorean hesitant fuzzy Hamacher hybrid average operator, and Pythagorean hesitant fuzzy Hamacher hybrid geometric operator. The prominent characteristics of these proposed operators are studied. Then, we utilize these operators to develop some approaches for solving the Pythagorean hesitant fuzzy multiple-attribute decision-making problems. Finally, a practical example is given to verify the developed approach and to demonstrate its practicality and effectiveness.

AN INTUITIONISTIC OWA OPERATOR

2004 ◽  
Vol 12 (06) ◽  
pp. 843-860 ◽  
Author(s):  
H. B. MITCHELL
Keyword(s):  
Fuzzy Sets ◽  
Multiple Criteria Decision Making ◽  
Weighted Average ◽  
Intuitionistic Fuzzy Sets ◽  
Extension Principle ◽  
Owa Operator ◽  
Simple Application ◽  
Average Operator ◽  
Ordered Weighted Average ◽  
Non Linear Operator

The OWA (Ordered Weighted Average) operator is a powerful non-linear operator for aggregating a set of inputs ai,i∈{1,2,…,M}. In the original OWA operator the inputs are crisp variables ai. This restriction was subsequently removed by Mitchell and Schaefer who by application of the extension principle defined a fuzzy OWA operator which aggregates a set of ordinary fuzzy sets Ai. We continue this process and define an intuitionistic OWA operator which aggregates a set of intuitionistic fuzzy sets Ãi. We describe a simple application of the new intuitionistic OWA operator in multiple-expert multiple-criteria decision-making.

INTUITIONISTIC FUZZY REASONING FOR MULTIPLE TWO-CLASS CLASSIFIERS FUSION

2012 ◽  
Vol 26 (07) ◽  
pp. 1250016 ◽  
Author(s):  
HAI WANG ◽  
GANG QIAN ◽  
XIANGQIAN FENG
Keyword(s):  
Test Pattern ◽  
Weighted Average ◽  
Fuzzy Reasoning ◽  
Fuzzy Arithmetic ◽  
Fusion Algorithm ◽  
Average Operator ◽  
Arithmetic Average ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Weighted Average ◽  
Ordered Weighted Average

Combining outputs of a pool of individual classifiers appropriately, as a hot research topic of pattern classification, can generate statistically significant increase in classification performances. During the last decades, several fusion algorithms were presented, but few of those focus on two-class classification which possesses wide application area such as sentiment classification, cancer differentiation and so on. Thus the main purpose of this paper is to develop a highly effective fusion algorithm, i.e. intuitionistic fuzzy reasoning fusion algorithm, to increase the performance of a multiple two-class classifiers system. The outputs of component classifiers are represented by a set of intuitionistic fuzzy values at first and the fusion process is interpreted as aggregation of intuitionistic fuzzy information. The proposed algorithm includes three versions using the intuitionistic fuzzy arithmetic average operator, intuitionistic fuzzy weighted average operator and induced intuitionistic fuzzy ordered weighted average aggregation operator, respectively. The proposed fusion algorithm can combine both evidences of the hypothesis that a test pattern belongs to a class and evidences of the hypothesis that the test pattern does not belong to the class at the same time. We compare the versions of our algorithm with four other fusion techniques on five open-access datasets. The proposed algorithm exhibits good predictive abilities, compared to the best individual classifiers and other comparable fusion techniques. Further, the experiments show some interesting results about measuring and weighting component classifiers.

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