On the Dempster-Shafer framework and new combination rules
Information Sciences: an International Journal
The Combination of Evidence in the Transferable Belief Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Grey system theory
The Journal of Grey System
Combining belief functions when evidence conflicts
Decision Support Systems
Combining belief functions based on distance of evidence
Decision Support Systems
3D target recognition using cooperative feature map binding under Markov Chain Monte Carlo
Pattern Recognition Letters
Robust automatic target recognition using learning classifier systems
Information Fusion
Multi-focus image fusion using pulse coupled neural network
Pattern Recognition Letters
A new technique for combining multiple classifiers using the dempster-shafer theory of evidence
Journal of Artificial Intelligence Research
Multisensor fusion in the frame of evidence theory for landmines detection
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A neural network classifier based on Dempster-Shafer theory
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Novel Cooperative Neural Fusion Algorithms for Image Restoration and Image Fusion
IEEE Transactions on Image Processing
Combination rule of D-S evidence theory based on the strategy of cross merging between evidences
Expert Systems with Applications: An International Journal
Information Technology and Management
Grey relational grade in local support vector regression for financial time series prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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In the framework of evidence theory, data fusion is to build a single belief function by combining several belief functions derived from distinct information sources. Dempster's rule of combination, a classical combination rule with several interesting mathematical properties, is widely employed. However, as an inherent problem, Dempster's rule of combination is incapable of managing the existing conflicts from various information sources at the step of normalization. The conflict management becomes an important problem in the operation of combination, especially in the condition of highly conflicting, which leads to produce the illogical result of combination. In this paper, we introduce the idea of grey relational analysis (GRA), and propose a new conflict reassignment approach of belief functions, as a preprocessing method, to automatically identify and reassign the conflicts on belief functions before combination. Three numerical examples are employed and implemented. Through analysis and comparison of the combined results, of the existing alternatives, the proposed approach not only can flexibly solve the problem of conflict management with better convergence performance, but also can automatically evaluate the reliability of the information sources and distinguish the unreliable information.