A Modified OWA Operator and its Use in Lossless DPCM Image Compression

Mitchell, H. B.; Estrakh, D. D.

doi:10.1142/s0218488597000324

Cited by 82 publications

(44 citation statements)

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“…Mitchell and Estrakh in [27] described a modified OWA operator in which the input arguments are not re-arranged according to their values but rather using a function of the arguments. Inspired by this work, Yager and Filev introduced in [49] a more general type of OWA operator, which they named the Induced OWA (IOWA) operator:…”

Section: Group Decision Makingmentioning

confidence: 99%

Modelling influence in group decision making

et al. 2016

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Group decision making has been widely studied since group decision making processes are very common in many fields. Formal representation of the experts' opinions, aggregation of assessments or selection of the best alternatives have been some of main areas addressed by scientists and researchers. In this paper, we focus on another promising area, the study of group decision making processes from the concept of influence and social networks. In order to do so, we present a novel model that gathers the experts' initial opinions and provides a framework to represent the influence of a given expert over the other(s). With this proposal it is feasible to estimate both the evolution of the group decision making process and the final solution before carrying out the group discussion process and consequently foreseeing possible actions.

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Section: Group Decision Makingmentioning

confidence: 99%

Modelling influence in group decision making

et al. 2016

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“…Mitchell and Estrakh in [6] described a modified OWA operator in which the input arguments are not rearranged according to their values but rather using a function of the arguments. Inspired by this work, Yager and Filev in [16] defined a more general type of OWA operator:…”

Section: Iowa Operatorsmentioning

confidence: 99%

IOWA & cross-ratio uninorm operators as aggregation tools in sentiment analysis and ensemble methods

Appel

Chiclana

Carter

et al. 2017

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Abstract-In the field of Sentiment Analysis, a number of different classifiers are utilised to attempt to establish the polarity of a given sentence. As such, there could be a need for aggregating the outputs of the algorithms involved in the classification effort. If the output of every classification algorithm resembles the opinion of an expert in the subject at hand, we are then in the presence of a group decisionmaking problem, which in turn translates into two subproblems: (a) defining the desired semantic of the aggregation of all opinions, and (b) applying the proper aggregation technique that can achieve the desired semantic chosen in (a). The objective of this article is twofold. Firstly, we present two specific aggregation semantics, namely fuzzy-majority and compensatory, which are based on Induced Ordered Weighted Averaging and Uninorm operators, respectively. Secondly, we show the power of these two techniques by applying them to an existing hybrid method for classification of sentiments at the sentence level. In this case, the proposed aggregation solutions act as a complement in order to improve the performance of the aforementioned hybrid method. In more general terms, the proposed solutions could be used in the creation of semantic-sensitive ensemble methods, instead of the more simple ensemble choices available today in commercial machine learning software offerings.

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“…Mitchell and Estrakh (1997) described a modified OWA operator in which the input arguments are not rearranged according to their values but rather using a function of the arguments [17]. Inspired by this work, Yager and Filev (1998) introduced a more general type of OWA operator, which they named the Induced Ordered Weighted Averaging (IOWA) operator [9].…”

Section: Preliminaries Owa and Iowa Operatorsmentioning

confidence: 99%