A Q-learning-based multi-agent system for data classification

Pourpanah, Farhad, Tan, Choo Jun, Lim, Chee Peng and Mohamad-Saleh, Junita 2017, A Q-learning-based multi-agent system for data classification, Applied soft computing, vol. 52, pp. 519-531, doi: 10.1016/j.asoc.2016.10.016.

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Title A Q-learning-based multi-agent system for data classification
Author(s) Pourpanah, Farhad
Tan, Choo Jun
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Mohamad-Saleh, Junita
Journal name Applied soft computing
Volume number 52
Start page 519
End page 531
Total pages 13
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2017-03
ISSN 1568-4946
Keyword(s) Fuzzy ARTMAP
Multi-agent system
Trust measurement
Data classification
Summary In this paper, a multi-agent classifier system with Q-learning is proposed for tackling data classification problems. A trust measurement using a combination of Q-learning and Bayesian formalism is formulated. Specifically, a number of learning agents comprising hybrid neural networks with Q-learning, which we have formulated in our previous work, are devised to form the proposed Q-learning Multi-Agent Classifier System (QMACS). The time complexity of QMACS is analyzed using the big O-notation method. In addition, a number of benchmark problems are employed to evaluate the effectiveness of QMACS, which include small and large data sets with and without noise. To analyze the QMACS performance statistically, the bootstrap method with 95% confidence interval is used. The results from QMACS are compared with those from its constituents and other models reported in the literature. The outcome indicates the effectiveness of QMACS in combining the predictions from its learning agents to improve the overall classification performance.
Language eng
DOI 10.1016/j.asoc.2016.10.016
Field of Research 0102 Applied Mathematics
0801 Artificial Intelligence And Image Processing
0806 Information Systems
Socio Economic Objective 0 Not Applicable
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2016, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30093349

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