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Applying a multi-agent classifier system with a novel trust measurement method to classifying medical data

Mohammed,MF, Lim,CP and Bt Ngah,UK 2014, Applying a multi-agent classifier system with a novel trust measurement method to classifying medical data. In Sakim, M, Amylia, H and Mustaffa, MT (ed), The 8th international conference on robotic, vision, signal processing & power applications : innovation excellence towards humanistic technology, Springer, Singapore, pp.355-362, doi: 10.1007/978-981-4585-42-2_41.

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Title Applying a multi-agent classifier system with a novel trust measurement method to classifying medical data
Author(s) Mohammed,MF
Lim,CPORCID iD for Lim,CP orcid.org/0000-0003-4191-9083
Bt Ngah,UK
Title of book The 8th international conference on robotic, vision, signal processing & power applications : innovation excellence towards humanistic technology
Editor(s) Sakim, M
Amylia, H
Mustaffa, MT
Publication date 2014
Series Lecture Notes in Electrical Engineering; v.291
Chapter number 41
Total chapters 60
Start page 355
End page 362
Total pages 8
Publisher Springer
Place of Publication Singapore
Keyword(s) Fuzzy min-max neural network
Medical data classification
Multi-agent classifier system
Trust measurement
Summary In this paper, we present the application of a Multi-Agent Classifier System (MACS) to medical data classification tasks. The MACS model comprises a number of Fuzzy Min-Max (FMM) neural network classifiers as its agents. A trust measurement method is used to integrate the predictions from multiple agents, in order to improve the overall performance of the MACS model. An auction procedure based on the sealed bid is adopted for the MACS model in determining the winning agent. The effectiveness of the MACS model is evaluated using the Wisconsin Breast Cancer (WBC) benchmark problem and a real-world heart disease diagnosis problem. The results demonstrate that stable results are produced by the MACS model in undertaking medical data classification tasks. © 2014 Springer Science+Business Media Singapore.
Notes Proceedings of the 8th International Conference on Robotics, Vision, Signal Processing & Power Applications (ROVISP 2013)
ISBN 9789814585415
ISSN 1876-1100
1876-1119
Language eng
DOI 10.1007/978-981-4585-42-2_41
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2014, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30070510

Document type: Book Chapter
Collection: Centre for Intelligent Systems Research
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