Combining belief functions when evidence conflicts
Decision Support Systems
Determining the dimensionality of multidimensional scaling representations for cognitive modeling
Journal of Mathematical Psychology
Techniques for Dealing with Missing Values in Classification
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
Possibilistic pattern recognition in a digestive database for mining imperfect data
WSEAS TRANSACTIONS on SYSTEMS
Fusion of possibilistic sources of evidences for pattern recognition
Integrated Computer-Aided Engineering
Multi-Sensor Data Fusion with MATLAB
Multi-Sensor Data Fusion with MATLAB
Gastroenterology dataset clustering using possibilistic Kohonen maps
WSEAS Transactions on Information Science and Applications
EVCLUS: evidential clustering of proximity data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A neural network classifier based on Dempster-Shafer theory
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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This study aims to build a general mathematical model in integrated security systems to overcome the remaining challenges in this domain, due to the heterogeneity, the uncertainty, the bad quality, and the conflict resulting from the information provided by the information sources (i.e. sensors) by taking account of the constraints, the scalability, and the architecture of the security system. The proposed model is fundamentally based on the proportional conflict redistribution fusion rule developed by Arnaud Martin under the framework of Dezert-Smarandache model. This combination permits to extend the utility of Dempster-Shafer model, and assures the generality of the proposed system.