Relative confidence based information fusion for Semg-based pattern recognition

Jinrong Li; Yinfeng Fang; Yong Ning; Jing  Jie; Ping Tan; Honghai Liu

doi:10.1109/ICMLC.2018.8526931

Relative confidence based information fusion for Semg-based pattern recognition

Jinrong Li, Yinfeng Fang, Yong Ning, Jing Jie, Ping Tan, Honghai Liu

University of Science and Technology of China

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

This study aims to establish a decision fusion scheme that synthesize the advantages of different classifiers and avoids uncertain decisions. Thus, relative confidence factors of each classifier was proposed to correct the classification decision made by each classifier, and the final classification result was derived by fusing all corrected decision based on the combination of Dempster-Shafer's rule. The novel fusion scheme is evaluated in the scenario of sEMG-based hand movement identification, in which five classffiers are adopted. The experimental results demonstrated that the novel scheme can obtain higher classification accuracy and stability than the other methods.

Original language	English
Title of host publication	2018 International Conference on Machine Learning and Cybernetics
Subtitle of host publication	ICMLC
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	625-630
ISBN (Electronic)	978-1-5386-5214-5
ISBN (Print)	978-1-5386-5215-2
DOIs	https://doi.org/10.1109/ICMLC.2018.8526931
Publication status	Published - 12 Nov 2018
Event	2018 International Conference on Machine Learning and Cybernetics - http://www.icmlc.com/icmlc/welcome.html, Chengdu, China Duration: 15 Jul 2018 → 18 Jul 2018

Publication series

Name	IEEE ICMLC Proceedings Series
Publisher	IEEE
ISSN (Print)	2160-133X
ISSN (Electronic)	2160-1348

Conference

Conference	2018 International Conference on Machine Learning and Cybernetics
Abbreviated title	ICMLC 2018
Country/Territory	China
City	Chengdu
Period	15/07/18 → 18/07/18

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10.1109/ICMLC.2018.8526931

paper_ICMLC2018
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Accepted author manuscript (Post-print), 1.06 MB

https://ieeexplore.ieee.org/document/8526931

Cite this

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title = "Relative confidence based information fusion for Semg-based pattern recognition",

abstract = "This study aims to establish a decision fusion scheme that synthesize the advantages of different classifiers and avoids uncertain decisions. Thus, relative confidence factors of each classifier was proposed to correct the classification decision made by each classifier, and the final classification result was derived by fusing all corrected decision based on the combination of Dempster-Shafer's rule. The novel fusion scheme is evaluated in the scenario of sEMG-based hand movement identification, in which five classffiers are adopted. The experimental results demonstrated that the novel scheme can obtain higher classification accuracy and stability than the other methods.",

author = "Jinrong Li and Yinfeng Fang and Yong Ning and Jing Jie and Ping Tan and Honghai Liu",

year = "2018",

month = nov,

day = "12",

doi = "10.1109/ICMLC.2018.8526931",

language = "English",

isbn = "978-1-5386-5215-2",

series = "IEEE ICMLC Proceedings Series",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "625--630",

booktitle = "2018 International Conference on Machine Learning and Cybernetics",

address = "United States",

note = "2018 International Conference on Machine Learning and Cybernetics, ICMLC 2018 ; Conference date: 15-07-2018 Through 18-07-2018",

}

Li, J, Fang, Y, Ning, Y, Jie, J, Tan, P & Liu, H 2018, Relative confidence based information fusion for Semg-based pattern recognition. in 2018 International Conference on Machine Learning and Cybernetics: ICMLC. IEEE ICMLC Proceedings Series, Institute of Electrical and Electronics Engineers Inc., pp. 625-630, 2018 International Conference on Machine Learning and Cybernetics, Chengdu, China, 15/07/18. https://doi.org/10.1109/ICMLC.2018.8526931

Relative confidence based information fusion for Semg-based pattern recognition. / Li, Jinrong; Fang, Yinfeng; Ning, Yong et al.
2018 International Conference on Machine Learning and Cybernetics: ICMLC. Institute of Electrical and Electronics Engineers Inc., 2018. p. 625-630 (IEEE ICMLC Proceedings Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Li, Jinrong

AU - Fang, Yinfeng

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AU - Liu, Honghai

PY - 2018/11/12

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N2 - This study aims to establish a decision fusion scheme that synthesize the advantages of different classifiers and avoids uncertain decisions. Thus, relative confidence factors of each classifier was proposed to correct the classification decision made by each classifier, and the final classification result was derived by fusing all corrected decision based on the combination of Dempster-Shafer's rule. The novel fusion scheme is evaluated in the scenario of sEMG-based hand movement identification, in which five classffiers are adopted. The experimental results demonstrated that the novel scheme can obtain higher classification accuracy and stability than the other methods.

AB - This study aims to establish a decision fusion scheme that synthesize the advantages of different classifiers and avoids uncertain decisions. Thus, relative confidence factors of each classifier was proposed to correct the classification decision made by each classifier, and the final classification result was derived by fusing all corrected decision based on the combination of Dempster-Shafer's rule. The novel fusion scheme is evaluated in the scenario of sEMG-based hand movement identification, in which five classffiers are adopted. The experimental results demonstrated that the novel scheme can obtain higher classification accuracy and stability than the other methods.

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BT - 2018 International Conference on Machine Learning and Cybernetics

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 International Conference on Machine Learning and Cybernetics

Y2 - 15 July 2018 through 18 July 2018

ER -

Relative confidence based information fusion for Semg-based pattern recognition

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