Evidential reasoning rule for evidence combination

Abstract:The single-valued neutrosophic set is a subclass of neutrosophic set, and has been proposed in recent years. An important application for single-valued neutrosophic sets is to solve multicriteria decision-making problems. The key to using neutrosophic sets in decision-making applications is to make a similarity measure between single-valued neutrosophic sets. In this paper, a new method to measure the similarity between single-valued neutrosophic sets using Dempster-Shafer evidence theory is proposed, and it is applied in multicriteria decision-making. Finally, some examples are given to show the reasonable and effective use of the proposed method.

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“…is defined in Equation (22). We can see that using basic probability assignment (BPA) can express the SNVS reasonably.…”

Section: Definition 17 Suppose Neutrosophic Set a Is An Snvs A Simpmentioning

confidence: 99%

A Novel Single-Valued Neutrosophic Set Similarity Measure and Its Application in Multicriteria Decision-Making

Jiang

Shou

2017

Symmetry

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“…To address this problem, Dezert et al [20,21] developed the Dezert-Smarandache Theory (DSmT) by introducing coefficient of reliabilities and weights, and makes the DSmT capable of combining pieces of evidence in complete confliction by redistributing the conflicts of evidences. However, DSmT neither keeps the consistencies with the Bayesian rule, nor is acknowledged as rigorous reasoning [22]. Originated from the Dempster's and Bayesian rule, Yang et al [22] developed the Evidential Reasoning (ER) rule in which the coefficients of weight and reliability are inherited from DSmT, and their discounting method are modified, hence there is no more deficiency in the inference process, meanwhile keeping the consistency with the Bayesian rule.…”

Section: A Proposed Approachmentioning

confidence: 99%

An Internet of Things Approach for Extracting Featured Data Using AIS Database: An Application Based on the Viewpoint of Connected Ships

Malekian

et al. 2017

Symmetry

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Automatic Identification System (AIS), as a major data source of navigational data, is widely used in the application of connected ships for the purpose of implementing maritime situation awareness and evaluating maritime transportation. Efficiently extracting featured data from AIS database is always a challenge and time-consuming work for maritime administrators and researchers. In this paper, a novel approach was proposed to extract massive featured data from the AIS database. An Evidential Reasoning rule based methodology was proposed to simulate the procedure of extracting routes from AIS database artificially. First, the frequency distributions of ship dynamic attributes, such as the mean and variance of Speed over Ground, Course over Ground, are obtained, respectively, according to the verified AIS data samples. Subsequently, the correlations between the attributes and belief degrees of the categories are established based on likelihood modeling. In this case, the attributes were characterized into several pieces of evidence, and the evidence can be combined with the Evidential Reasoning rule. In addition, the weight coefficients were trained in a nonlinear optimization model to extract the AIS data more accurately. A real life case study was conducted at an intersection waterway, Yangtze River, Wuhan, China. The results show that the proposed methodology is able to extract data very precisely.

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“…It has extended the Dempster-Shafer theory [35,36]. DempsterShafer theory is with advantages to handle uncertain information [37,38,39,40,41,42,43,44], and is extensively used in many fields, such as risk assessment [45,46], expert systems [47,48], classification and clustering [49,50], parameter estimation [51], decision making [52,53,54], and so forth [55,56,57,58,59,60,61]. However, there are some weaknesses in DempsterShafer theory.…”

Section: Numbersmentioning

confidence: 99%

D-CFPR: D numbers extended consistent fuzzy preference relations

Deng

Chan

et al. 2015

Knowledge-Based Systems

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How to express an expert's or a decision maker's preference for alternatives is an open issue. Consistent fuzzy preference relation (CFPR) is with big advantages to handle this problem due to it can be construed via a smaller number of pairwise comparisons and satisfies additive transitivity property.However, the CFPR is incapable of dealing with the cases involving uncertain and incomplete information. In this paper, a D numbers extended consistent fuzzy preference relation (D-CFPR) is proposed to overcome the weakness.

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Evidential reasoning rule for evidence combination

Cited by 563 publications

References 32 publications

A Novel Single-Valued Neutrosophic Set Similarity Measure and Its Application in Multicriteria Decision-Making

A Novel Single-Valued Neutrosophic Set Similarity Measure and Its Application in Multicriteria Decision-Making

An Internet of Things Approach for Extracting Featured Data Using AIS Database: An Application Based on the Viewpoint of Connected Ships

D-CFPR: D numbers extended consistent fuzzy preference relations

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