On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty

By Jianbo Yang, Dongling Xu. IEEE Trans. Syst. Man Cybern. Part A, 2002

903 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f2352bde549923162faa34e50ea8b412884fefd3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

1. Dongling Xu
2. Ian N. Durbach
3. Jianbo Yang
4. T. Stewart
5. Hai Wang
6. Zeshui Xu
7. Kwai-Sang Chin
8. Yingming Wang

In Statistics and Probability

1. Jianbo Yang
2. T. Stewart
3. Ian N. Durbach
4. Dongling Xu
5. Yingming Wang
6. Kwai-Sang Chin
7. Jin Wang
8. Jun Liu

Citations in the corpus

• Modeling uncertainty in multi-criteria decision analysis (2012, methodology): “The algorithm, described fully in [156], is based on first multiplying marginal beliefs with importance weights on each attribute, i.”
• Inference and learning methodology of belief-rule-based expert system for pipeline leak detection (2007, methodology): “The belief rule base concept and its inference methodology were proposed by Yang, Liu, Wang, Sii, and Wang (2005a) based on the Evidential Reasoning approach (Yang, 2001; Yang & Xu, 2002a, 2002b ).”
• Probabilistic linguistic term sets in multi-attribute group decision making (2016, background): “Moreover, Yang and Xu [36, 37] considered partial ignorance as well (in which the probabilistic information is interpreted as the degrees of belief) under the framework of evidential reasoning.”
• The evidential reasoning approach for MADA under both probabilistic and fuzzy uncertainties (2006, methodology): “The belief degree bn(al) stands for the lower bound of the likelihood that al is assessed to Hn, whilst the corresponding upper bound of the likelihood is given by (bn(al )+ bH(al)) (Yang, 2001; Yang and Xu, 2002a ), which leads to the establishment of a utility interval if the assessment is incomp”
• Belief rule-base inference methodology using the evidential reasoning Approach-RIMER (2006, methodology): “, L into basic probability masses using the following ER algorithm [17], [18]:”
• Evidential reasoning rule for evidence combination (2013, methodology): “In the ER approach [41], a basic probability mass is generated by multiplying the degree of belief by the weight of evidence.”