Combination of Classifiers With Different Frames of Discernment Based on Belief Functions

IEEE Trans. Fuzzy Syst.

Cao

Jolfaei

2021

143

Dempster-Shafer evidence (DSE) theory, which allows combining pieces of evidence from different data sources to derive a degree of belief function that is a type of fuzzy measure, is a general framework for reasoning with uncertainty. In this framework, how to optimally manage the conflicts of multiple pieces of evidence in DSE remains an open issue to support decision making. The existing conflict measurement approaches can achieve acceptable outcomes but do not fully consider the optimization at the decision-making level using the novel measurement of conflicts. In this paper, we proposed a novel evidential correlation coefficient (ECC) for belief functions by measuring the conflict between two pieces of evidence in decision making. Then, we investigated the properties of our proposed evidential correlation and conflict coefficients, which are all proven to satisfy the desirable properties for conflict measurement, including nonnegativity, symmetry, boundedness, extreme consistency, and insensitivity to refinement. We also presented several examples and comparisons to demonstrate the superiority of our proposed ECC method. Finally, we applied the proposed ECC in a decision-making application of motor rotor fault diagnosis, which verified the practicability and effectiveness of our proposed novel measurement.

Section: Preliminariesmentioning

confidence: 99%

A Novel Conflict Measurement in Decision-Making and Its Application in Fault Diagnosis

IEEE Trans. Fuzzy Syst.

Cao

Jolfaei

2021

143

“…However, in the framework of D‐S evidence theory, the classic Dempster's combination rule fails to fuse information correctly when facing highly conflicting evidence 50 . Hence, there are many studies that focus on how to fuse conflicting evidence correctly 51 – 57 …”

Section: Introductionmentioning

confidence: 99%

“…50 Hence, there are many studies that focus on how to fuse conflicting evidence correctly. [51][52][53][54][55][56][57] To sum up, these solutions can be generally divided into two categories, namely, refining data used in combination and altering the rules of combination. In the first category, the typical examples are Murphy's average approach 58 and Deng et al's improved method 59 which inherits from Murphy's method and further develops it.…”

mentioning

confidence: 99%

Conflicting management of evidence combination from the point of improvement of basic probability assignment

2021

Int J Intell Syst

An open issue of the Dempster combination rule is the conflicting management, which is very important in multisource data fusion, such as group decision making and target recognition. To address this issue, an improved method to generate basic probability assignment is presented. Then, a new combination method to assign the conflicting mass function without the normalization is proposed to handle a highly conflicting environment. Compared with other methods, this proposed method is convenient in computing and has better accuracy to predict potential possibilities especially when disposing of extreme status. Some numerical examples and real benchmark data collected in UCI database are illustrated to verify the validity and rationality of the proposed method.

“…Hence, evidence theory has better ability to deal with uncertain information than traditional probability theory [37,38].However, there are many open issues in Dempster-Shafer eviednce theory. For example, it can lead to counterintuitive results when the evidence is highly conflicting [39][40][41], how to determine the basic assignment [42][43][44],how to measure the uncertainty of the evidence [45], and so on. Many applications are developed under the framework of Dempster-Shafer evidence theory [46][47][48][49].…”

Section: Introductionmentioning

confidence: 99%

CBD: A New Divergence Measure for Complex Mass Function and its Application in Pattern Recognition

Luo

2021

Preprint

The theory of complex mass function is an effective method to deal with uncertainty information, and it is a generalized of Dempster-Shafer evidence theory. However, divergence measure is still an open issue in the realm of complex mass function theory. The main contribution of our paper is to propose a generalized divergence measure for complex mass function that is called complex belief divergence (CBD),which has the properties of symmetry, nonnegativity, nondegeneracy. When complex mass function degenerates into classical mass function, the CBD will degenerate into classical belief divergence, which has a better ability to measure uncertainty of information. Finally, a pattern recognition algorithm based on CBD is designed and applied to a medical diagnosis problem, which proves its practical prospect.