research-article

An improved fuzzy ARTMAP and Q-learning agent model for pattern classification

Authors:

Farhad Pourpanah,

Manjeevan Seera,

Choo Jun TanAuthors Info & Claims

Volume 359, Issue C

Pages 139 - 152

https://doi.org/10.1016/j.neucom.2019.06.002

Published: 24 September 2019 Publication History

Highlights

•

An improved multi-agent system based on Fuzzy ARTMAP (FAM) and Q-learning, i.e., IQ-MACS, is proposed.

•

A trust measurement using a combination of Q-learning and Bayesian formalism is added at team level.

•

Two real-world case studies, i.e., human motion detection and motor fault detection, with and without noise are used to evaluate the proposed model.

•

The outcome indicates that IQMACS is able to produce promising results.

Abstract

The Fuzzy ARTMAP (FAM) network is an online supervised neural network that operates by computing the similarity level between the new sample and those prototype nodes stored in its network against a threshold. In our previous study, we have developed a multi-agent system consisting of an ensemble of FAM networks and Q-learning, known as QMACS, for data classification. In this paper, an Improved QMACS (IQMACS) model with trust measurement using a combination of Q-learning and Bayesian formalism is proposed. A number of benchmark and real-world problems, i.e., motor fault detection and human motion detection, are conducted to evaluate the effectiveness of IQMACS. Statistical features are extracted from real-world case studies and utilized for classification with IQMACS, QMACS, and their constituents. The experimental results indicate that IQMACS produces better classification performance by combining the outcomes of its constituents as compared with those of QMACS and other related methods.

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Cited By

Pourpanah FEtemad A(2024)Exploring the Landscape of Ubiquitous In-home Health Monitoring: A Comprehensive SurveyACM Transactions on Computing for Healthcare10.1145/36708545:4(1-43)Online publication date: 23-Oct-2024
https://dl.acm.org/doi/10.1145/3670854
Ma ZLi XLiu ZHuang RHe N(2024)Model checking fuzzy computation tree logic of multi-agent systems based on fuzzy interpreted systemsFuzzy Sets and Systems10.1016/j.fss.2024.108966485:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.fss.2024.108966
Pourpanah FAbdar MLuo YZhou XWang RLim CWang XWu Q(2023)A Review of Generalized Zero-Shot Learning MethodsIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.319169645:4(4051-4070)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/TPAMI.2022.3191696
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Index Terms

An improved fuzzy ARTMAP and Q-learning agent model for pattern classification
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Probabilistic reasoning
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees
      2. Neural networks

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Neurocomputing

Neurocomputing Volume 359, Issue C

Sep 2019

528 pages

ISSN:0925-2312

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 24 September 2019

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Cited By

Pourpanah FEtemad A(2024)Exploring the Landscape of Ubiquitous In-home Health Monitoring: A Comprehensive SurveyACM Transactions on Computing for Healthcare10.1145/36708545:4(1-43)Online publication date: 23-Oct-2024
https://dl.acm.org/doi/10.1145/3670854
Ma ZLi XLiu ZHuang RHe N(2024)Model checking fuzzy computation tree logic of multi-agent systems based on fuzzy interpreted systemsFuzzy Sets and Systems10.1016/j.fss.2024.108966485:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.fss.2024.108966
Pourpanah FAbdar MLuo YZhou XWang RLim CWang XWu Q(2023)A Review of Generalized Zero-Shot Learning MethodsIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.319169645:4(4051-4070)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/TPAMI.2022.3191696
Zhou XLiu HPourpanah FZeng TWang X(2022)A survey on epistemic (model) uncertainty in supervised learningNeurocomputing10.1016/j.neucom.2021.10.119489:C(449-465)Online publication date: 22-Jun-2022
https://dl.acm.org/doi/10.1016/j.neucom.2021.10.119
Soundirarajan MPakniyat NSim SNathan VNamazi H(2021)Information-based analysis of the relationship between brain and facial muscle activities in response to static visual stimuliTechnology and Health Care10.3233/THC-19208529:1(99-109)Online publication date: 1-Jan-2021
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Abdar MPourpanah FHussain SRezazadegan DLiu LGhavamzadeh MFieguth PCao XKhosravi AAcharya UMakarenkov VNahavandi S(2021)A review of uncertainty quantification in deep learningInformation Fusion10.1016/j.inffus.2021.05.00876:C(243-297)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1016/j.inffus.2021.05.008
Yeganeh APourpanah FShadman A(2021)An ANN-based ensemble model for change point estimation in control chartsApplied Soft Computing10.1016/j.asoc.2021.107604110:COnline publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1016/j.asoc.2021.107604
Gholami JMardukhi FZawbaa H(2021)An improved crow search algorithm for solving numerical optimization functionsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-021-05827-w25:14(9441-9454)Online publication date: 1-Jul-2021
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