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A Q-learning-based multi-agent system for data classification
journal contribution
posted on 2017-03-01, 00:00 authored by F Pourpanah, C J Tan, Chee Peng LimChee Peng Lim, J Mohamad-SalehIn 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.
History
Journal
Applied soft computingVolume
52Pagination
519 - 531Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
ISSN
1568-4946Language
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2016, ElsevierUsage metrics
Categories
Keywords
Fuzzy ARTMAPMulti-agent systemQ-learningTrust measurementData classificationScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Interdisciplinary ApplicationsComputer ScienceNEURAL-NETWORK ENSEMBLESARCHITECTURECLASSIFIERSBOOTSTRAPARTMAPInformation SystemsArtificial Intelligence and Image Processing
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