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A neural network-based multi-agent classifier system with a Bayesian formalism for trust measurement

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Abstract

In this paper, a neural network (NN)-based multi-agent classifier system (MACS) utilising the trust-negotiation-communication (TNC) reasoning model is proposed. A novel trust measurement method, based on the combination of Bayesian belief functions, is incorporated into the TNC model. The Fuzzy Min-Max (FMM) NN is used as learning agents in the MACS, and useful modifications of FMM are proposed so that it can be adopted for trust measurement. Besides, an auctioning procedure, based on the sealed bid method, is applied for the negotiation phase of the TNC model. Two benchmark data sets are used to evaluate the effectiveness of the proposed MACS. The results obtained compare favourably with those from a number of machine learning methods. The applicability of the proposed MACS to two industrial sensor data fusion and classification tasks is also demonstrated, with the implications analysed and discussed.

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering Technology, Al-Balqa’ Applied University, Al-Salt, Jordan

    Anas Quteishat

  2. School of Electrical and Electronic Engineering, University of Science Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia

    Chee Peng Lim & Junita Mohamad Saleh

  3. School of Electrical and Information Engineering, University of South Australia, Adelaide, Australia

    Jeffrey Tweedale & Lakhmi C. Jain

Authors
  1. Anas Quteishat
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  2. Chee Peng Lim
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  3. Junita Mohamad Saleh
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  4. Jeffrey Tweedale
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  5. Lakhmi C. Jain
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Correspondence to Chee Peng Lim.

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Quteishat, A., Lim, C.P., Saleh, J.M. et al. A neural network-based multi-agent classifier system with a Bayesian formalism for trust measurement. Soft Comput 15, 221–231 (2011). https://doi.org/10.1007/s00500-010-0592-0

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