A novel trust measurement method based on certified belief in strength for a multi-agent classifier system

Mohammed, Mohammed Falah, Lim, Chee Peng and Quteishat, Anas 2012, A novel trust measurement method based on certified belief in strength for a multi-agent classifier system, Neural computing and applications, pp. 1-9.

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Title A novel trust measurement method based on certified belief in strength for a multi-agent classifier system
Author(s) Mohammed, Mohammed Falah
Lim, Chee Peng
Quteishat, Anas
Journal name Neural computing and applications
Start page 1
End page 9
Total pages 9
Publisher Springer
Place of publication London, England
Publication date 2012-10
ISSN 1433-3058
0941-0643
Keyword(s) classification accuracy
fuzzy min-max neural network
multi-agent classifier system
strength and reputation
trust
Summary A novel trust measurement method, namely, certified belief in strength (CBS), for a multi-agent classifier system (MACS) is proposed in this paper. The CBS method aims to improve the performance of the constituent agents of the MACS, viz., the fuzzy min-max (FMM) neural network classifier. Trust measurement is accomplished using reputation and strength of the constituent agents. Trust is built from strong elements that are associated with the FMM agents, allowing the CBS method to improve the performance of the MACS. An auction procedure based on the sealed bid, namely, the first price method, is adopted for the MACS in determining the winning agent. The effectiveness of the CBS method and the bond (based on trust) is verified by using a number of benchmark data sets. The results demonstrate that the proposed MACS-CBS model is able to produce better accuracy and stability as compared with those from other existing methods. © 2012 Springer-Verlag London.
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category C1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30050992

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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