Base belief function: an efficient method of conflict management

Dempster–Shafer evidence theory is widely used in many applications such as decision making and pattern recognition. However, Dempster’s combination rule often produces results that do not reflect the actual distribution of belief when collected evidence highly conflicts each other. In this paper, a...

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Veröffentlicht in:Journal of ambient intelligence and humanized computing Jg. 10; H. 9; S. 3427 - 3437
Hauptverfasser: Wang, Yunjuan, Zhang, Kezhen, Deng, Yong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2019
Springer Nature B.V
Schlagworte:
Artificial Intelligence
Computational Intelligence
Decision making
Decision theory
Engineering
Fuzzy logic
Fuzzy sets
Hypotheses
Machine learning
Methods
Original Research
Pattern recognition
Probability
Robotics and Automation
User Interfaces and Human Computer Interaction
Conflicting evidence
Base belief function
Dempster–Shafer evidence theory
Belief function
ISSN:1868-5137, 1868-5145
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Zusammenfassung:Dempster–Shafer evidence theory is widely used in many applications such as decision making and pattern recognition. However, Dempster’s combination rule often produces results that do not reflect the actual distribution of belief when collected evidence highly conflicts each other. In this paper, a base belief function is proposed to modify the classical basic probability assignment before combination in closed-world. Base belief function focuses on making combination result intuitive especially when evidences highly conflict each other. Compared to other methods, the combination result produced by proposed method is logical and consistent with real world with less computational complexity and better performance. The advantage of base belief function is that it can avoid high conflicts between evidences and is especially suitable for the situation where the evidences appear in sequence. Several numerical examples as well as experiments using real data sets from the UCI machine learning repository for classification are employed to verify the rationality of the proposed method.
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ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-018-1099-2