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

Abstract

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.

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Acknowledgments

The authors gratefully acknowledge the partial financial support of the FRGS grants (No. 6711229 and 6711195) for this work.

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Correspondence to Chee Peng Lim.

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Mohammed, M.F., Lim, C.P. & Quteishat, A. A novel trust measurement method based on certified belief in strength for a multi-agent classifier system. Neural Comput & Applic 24, 421–429 (2014). https://doi.org/10.1007/s00521-012-1245-2

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  • DOI: https://doi.org/10.1007/s00521-012-1245-2

Keywords

  • Trust
  • Strength and reputation
  • Fuzzy min–max neural network
  • Multi-agent classifier system
  • Classification accuracy