Evidence Gathering for Hypothesis Resolution Using Judicial Evidential Reasoning

Jaunzemis, Andris D.; Holzinger, Marcus J.; Chan, Moses W.; Shenoy, Prakash P.

doi:10.23919/icif.2018.8455642

Cited by 13 publications

(15 citation statements)

References 16 publications

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“…Many methods are presented to update the status with collected information, including Beyasian updating rule, Dempster combination rule, negation, dynamic model such as DEMATEL, neural model, and so on . The likelihood function is first defined as the product of probabilities.…”

Section: Preliminariesmentioning

confidence: 99%

A new soft likelihood function based on power ordered weighted average operator

Song

Deng

2019

Int J Intell Syst

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The likelihood function is widely used in data processing but the classical likelihood function is too strict to deal with data with extreme values in real applications.To address this issue, a soft likelihood function based on power ordered weighted average (POWA) operator for probability distribution is proposed. Compared with the existing soft likelihood functions, one of the main features of the proposed likelihood function is that the weighted mean is displayed by the geometric mean of the weighted probability products. As a result, the effect on final result caused by extreme data can be efficiently decreased. Numerical examples are used to illustrate the efficiency of the proposed soft likelihood function. K E Y W O R D S geometric mean, likelihood function, OWA operator, POWA operator, soft likelihood function | INTRODUCTIONDealing with uncertainty information plays an important role in real applications. 1-3 Many math tools, such as fuzzy sets, 4-6 rough sets, 7 Z-numbers, 8-11 belief structures, 12-14 D numbers 15-17 entropy function, 18,19 and evidence theory, 20-22 are presented to handle different types of uncertainty information. Among these tools, probability is heavily studied and is widely used in many applications, including decision making, 23-26 estimation, 27,28 and uncertain measurement. [29][30][31][32] One of the most used models in probability is the likelihood function, which is used when data are available to describe plausibility of a parameter value. [33][34][35] However, it is very strict in real applications since the original likelihood function made up with the probability product is too strict to show the compatibility probabilities for the different pieces of evidence since any piece of probabilities, which are low-value would greatly reduce the likelihood function.How to cite this article: Song Y, Deng Y. A new soft likelihood function based on power ordered weighted average operator. Int J Intell

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Section: Preliminariesmentioning

confidence: 99%

A new soft likelihood function based on power ordered weighted average operator

Song

Deng

2019

Int J Intell Syst

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“…In addition, the Dempster rule can efficiently combine the sensor reports from different sources [79]. Due to the desirable properties, evidence theory has been accepted as de facto standard in decision making [10,28,78], risk and reliability analysis [13,27,40], system optimization [67] and pattern recognition [23,24,34,46,57].…”

Section: Introductionmentioning

confidence: 99%

Combination of Evidential Sensor Reports with Distance Function and Belief Entropy in Fault Diagnosis

Dong

Zhang

et al. 2019

INT J COMPUT COMMUN

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Although evidence theory has been applied in sensor data fusion, it will have unreasonable results when handling highly conflicting sensor reports. To address the issue, an improved fusing method with evidence distance and belief entropy is proposed. Generally, the goal is to obtain the appropriate weights assigning to different reports. Specifically, the distribution difference between two sensor reports is measured by belief entropy. The diversity degree is presented by the combination of evidence distance and the distribution difference. Then, the weight of each sensor report is determined based on the proposed diversity degree. Finally, we can use Dempster combination rule to make the decision. A real application in fault diagnosis and an example show the efficiency of the proposed method. Compared with the existing methods, the method not only has a better performance of convergence, but also less uncertainty.

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“…Thus, when it deals with uncertain issues, all the rules should take uncertainty into consideration. Dempster-Shafer evidence theory (D–S) [ 17 , 18 ] is widely used in many applications such as decision making [ 19 , 20 , 21 , 22 , 23 , 24 ], evidential reasoning [ 25 , 26 , 27 , 28 ], information fusion [ 29 , 30 ], pattern recognition [ 31 , 32 , 33 ], fault diagnosis [ 34 , 35 , 36 , 37 ], risk evaluation [ 38 , 39 , 40 ], network analysis [ 41 ], conflicting management [ 42 , 43 , 44 , 45 ], uncertainty modeling [ 46 , 47 , 48 , 49 , 50 ], and so on [ 51 , 52 , 53 , 54 ]. In the D–S evidence theory, Basic Belief Assignment (BBA) measures the uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Evidential Decision Tree Based on Belief Entropy

Deng

2019

Entropy

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Decision Tree is widely applied in many areas, such as classification and recognition. Traditional information entropy and Pearson’s correlation coefficient are often applied as measures of splitting rules to find the best splitting attribute. However, these methods can not handle uncertainty, since the relation between attributes and the degree of disorder of attributes can not be measured by them. Motivated by the idea of Deng Entropy, it can measure the uncertain degree of Basic Belief Assignment (BBA) in terms of uncertain problems. In this paper, Deng entropy is used as a measure of splitting rules to construct an evidential decision tree for fuzzy dataset classification. Compared to traditional combination rules used for combination of BBAs, the evidential decision tree can be applied to classification directly, which efficiently reduces the complexity of the algorithm. In addition, the experiments are conducted on iris dataset to build an evidential decision tree that achieves the goal of more accurate classification.

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Evidence Gathering for Hypothesis Resolution Using Judicial Evidential Reasoning

Cited by 13 publications

References 16 publications

A new soft likelihood function based on power ordered weighted average operator

A new soft likelihood function based on power ordered weighted average operator

Combination of Evidential Sensor Reports with Distance Function and Belief Entropy in Fault Diagnosis

Evidential Decision Tree Based on Belief Entropy

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