A mass assignment theory of the probability of fuzzy events

Baldwin, JF; Lawry, Jonathan; Martin, T. P.

doi:10.1016/0165-0114(95)00297-9

Cited by 91 publications

(59 citation statements)

References 12 publications

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“…In this paper we build the function by using opinions as estimates of the probabilities of a fuzzy variable (the reputation, which takes values in the [0,1] interval) to fall in a given range. This is consistent with mass-assignment based probability theory [14], and could in the future enable composing reputation with other random events having a PMF. This would also open the way to using conditional probability models to relate reputation to context.…”

Section: Reputationsupporting

confidence: 59%

A Privacy Preserving Reputation Protocol for Web Service Provider Selection

Hasan¹,

Brunie²,

Damiani³

2011

2011 IEEE 35th Annual Computer Software and Applications Conference Workshops

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Abstract-Reputation systems offer web service consumers an effective solution for selecting web service providers who meet their Quality of Service (QoS) expectations. A reputation system computes the reputation of a provider as an aggregate of the feedback submitted by consumers. Truthful feedback is clearly a pre-requisite for accurate reputation scores. However, it has been observed that users of a reputation system often hesitate in providing truthful feedback, mainly due to the fear of reprisal from target entities. We present a privacy preserving reputation protocol that enables web service consumers to provide feedback about web service providers in a private and thus uninhibited manner.

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Section: Reputationsupporting

confidence: 59%

A Privacy Preserving Reputation Protocol for Web Service Provider Selection

Hasan¹,

Brunie²,

Damiani³

2011

2011 IEEE 35th Annual Computer Software and Applications Conference Workshops

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“…In order to obtain such a measure we propose to generate a conceptual description of each distribution, in terms of fuzzy sets, so that a degree of match can then be determined according to the mass assignment theory of the probability of fuzzy events. 6 The notion of fuzzy constraints on attributes encoding probability distributions was first proposed in this context by Baldwin,7,8 and the idea has been utilized in a number of machine learning methods, many of which form part of the Fril Databrowser. 8 Previous methods, however, have involved the generation of only a single class prototype albeit in terms of compound attributes in some approaches.…”

Section: Introductionmentioning

confidence: 99%

“…This algorithm is also based on the theory of the probability of fuzzy events mentioned previously. 6 In view of these properties and our need for models defined within an uncertainty calculus, Fril provides an ideal implementation language for our learning algorithm, as well as providing a good knowledge representation framework for the models generated. Indeed, each prototype can be represented by a Fril rule and the built-in uncertainty calculus can be used directly for inference.…”

Section: Introductionmentioning

confidence: 99%

A mass assignment method for prototype induction

Baldwin

Lawry

Martin

1999

Int. J. Intell. Syst.

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“…That is where both types of uncertainties are present. This suggests the need for a theory of the probability of fuzzy events [21].…”

Section: Literature Reviewmentioning

confidence: 99%

A Fuzzy Probabilistic Maximum Technique to Optimize an Unconstrained Utility Based Multi Objective Model

Torkjazi¹,

Fazlollahtabar²

2015

Ind Eng Manage

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Research that considers uncertainty can be categorized according to the four primary approaches [14]:(1) Stochastic programming approach, (2) Fuzzy programming approach, AbstractOne way to optimize multi objective mathematical models is to employ utility functions for the objectives. Recent studies on utility based multi objective optimization consider only one utility function for each objective. But, in reality it is not reasonable to have a unique utility function corresponding to each objective function. Here, an unconstrained multi objective mathematical model is considered in which several utility functions are associated for each objective. A fuzzy probabilistic approach is incorporated to investigate the uncertainty of the utility functions for each objective. Since these utility functions are uncertain and in fuzzy form, the total utility function of the problem is a fuzzy nonlinear mathematical model. While, there are not any conventional approaches to solve such a model, a defuzzification method to change the total utility function to a crisp nonlinear model is employed. Meanwhile, maximum technique is applied to defuzzify the conditional utility functions. Then, an existing method to optimize the final single objective nonlinear model is adapted.The obtained results show that by changing the utility functions regarding to the dynamism of the environment, the method is still capable to provide the solutions accordingly. The effectiveness of the proposed approach is shown by solving a test problem. Citation: Torkjazi M, Fazlollahtabar H (2015) A Fuzzy Probabilistic Maximum Technique to Optimize an Unconstrained Utility Based Multi ObjectiveModel. Ind Eng Manage 4: 147.

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A mass assignment theory of the probability of fuzzy events

Cited by 91 publications

References 12 publications

A Privacy Preserving Reputation Protocol for Web Service Provider Selection

A Privacy Preserving Reputation Protocol for Web Service Provider Selection

A mass assignment method for prototype induction

A Fuzzy Probabilistic Maximum Technique to Optimize an Unconstrained Utility Based Multi Objective Model

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