A Modified Method for Evaluating Sustainable Transport Solutions Based on AHP and Dempster–Shafer Evidence Theory

Dempster‐Shafer is widely used to address the problems of uncertainty. One assumption mentioned in this theory is that the distribution of information should be independent. In practice, the requirement cannot be fulfilled. One of the efficient methods to deal with dependent evidence is to calculate the correlation discounting. However, existing coefficient can only be applied to show the direct relation between evidence A and B but do not take the indirect relationship into consideration. To address this issue, in this paper, a new method to combine dependent evidence based on decision‐making trial and evaluation laboratory is presented, not only considering the relation between evidence A and B and the relation between evidence B and C, but also considering the transitive influence between evidence A and C. Finally, the experiments on some benchmark data sets are illustrated to show the efficiency of the proposed method.

Section: Preliminariesmentioning

confidence: 99%

Section: Preliminariesmentioning

confidence: 99%

Dependent evidence combination based on decision‐making trial and evaluation laboratory method

Deng

2019

“…The Dempster‐Shafer evidence theory (D‐S theory) has been widely focused on and adopted in recent years for its great advantages in handling and analyzing uncertain information . This theory is widely used in object classification, decision making, uncertainty modeling, and information fusion . In these applications, Dempster's combination rule is very important to combine multiple sources for an aggregated result.…”

Section: Introductionmentioning

confidence: 99%

A new divergence measure for basic probability assignment and its applications in extremely uncertain environments

Fei

Deng

2018

Information fusion under extremely uncertain environments is an important issue in pattern classification and decision‐making problems. The Dempster‐Shafer evidence theory (D‐S theory) is more and more extensively applied in dealing with uncertain information. However, the results contrary to common sense are often obtained when combining different evidence using Dempster's combination rule. How to measure the difference between different evidence is still an open issue. In this paper, a new divergence is proposed based on the Kullback‐Leibler divergence to measure the difference between different basic probability assignments (BPAs). Numerical examples are used to illustrate the computational process of the proposed divergence. Then, the similarity for different BPAs is also defined based on the proposed divergence. The basic knowledge about pattern recognition is introduced, and a new classification algorithm is presented using the proposed divergence and similarity under extremely uncertain environments. The effectiveness of the classification algorithm is illustrated by a small example handling robot sensing. The proposed method is motivated by the urgent need to develop intelligent systems, such as sensor‐based data fusion manipulators, which are required to work in complicated, extremely uncertain environments. Sensory data satisfy the conditions (1) fragmentary and (2) collected from multiple levels of resolution.

“…It is necessary to consider the information quality (IQ) in the information fusion process. With the aid of the IQ, the fusion result will be more reasonable which is applied in fault diagnosis …”

Section: Introductionmentioning

confidence: 99%

“…With the aid of the IQ, the fusion result will be more reasonable. [26][27][28][29] Recently, Yager proposed a new IQ, 30 which is applied in fault diagnosis. 31 However, the existing IQ only measures the uncertainty of the information itself, while the association of the collected data is not considered.…”

mentioning

confidence: 99%

Aggregation of uncertainty data based on ordered weighting aggregation and generalized information quality

Xiao

2019

It is necessary to take the information quality into consideration in information fusion. However, existing information quality has a limitation due to the ignorance of the credibility of information source. In this paper, the generalized information quality (GIQ) been proposed considering the association among the collected sensor reports, first. Then, the ordered weighting aggregation (OWA) operator of probability distribution based on the GIQ is presented. Numerical example and real application in fault diagnosis are used to illustrate the efficiency of the proposed method. The proposed GIQ and OWA algorithm has the advantage in fault tolerance and reduce the impact of conflict data in data fusion process, due to the consideration of the credibility of each sensor report. The proposed method has the promising aspects in uncertainty data aggregation.