A Choquet Fuzzy Integral Vertical Bagging Classifier for Mobile Telematics Data Analysis

Siami, M; Naderpour, M; Lu, J

A Choquet Fuzzy Integral Vertical Bagging Classifier for Mobile Telematics Data Analysis

Siami, M Naderpour, M

Lu, J

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Fuzzy Systems, 2019, 2019-June
Issue Date:: 2019-06-01

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Field	Value	Language
dc.contributor.author	Siami, M	en_US
dc.contributor.author	Naderpour, M https://orcid.org/0000-0002-3549-3875	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date.available	2021-08-12T19:00:47Z
dc.date.issued	2019-06-01	en_US
dc.identifier.citation	IEEE International Conference on Fuzzy Systems, 2019, 2019-June	en_US
dc.identifier.isbn	9781538617281	en_US
dc.identifier.issn	1098-7584	en_US
dc.identifier.uri	http://hdl.handle.net/10453/137398
dc.description.abstract	© 2019 IEEE. Mobile app development in recent years has resulted in new products and features to improve human life. Mobile telematics is one such development that encompasses multidisciplinary fields for transportation safety. The application of mobile telematics has been explored in many areas, such as insurance and road safety. However, to the best of our knowledge, its application in gender detection has not been explored. This paper proposes a Choquet fuzzy integral vertical bagging classifier that detects gender through mobile telematics. In this model, different random forest classifiers are trained by randomly generated features with rough set theory, and the top three classifiers are fused using the Choquet fuzzy integral. The model is implemented and evaluated on a real dataset. The empirical results indicate that the Choquet fuzzy integral vertical bagging classifier outperforms other classifiers.	en_US
dc.relation.ispartof	IEEE International Conference on Fuzzy Systems	en_US
dc.relation.isbasedon	10.1109/FUZZ-IEEE.2019.8858812	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	A Choquet Fuzzy Integral Vertical Bagging Classifier for Mobile Telematics Data Analysis	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2019-June	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2019-June	en_US

Abstract:

© 2019 IEEE. Mobile app development in recent years has resulted in new products and features to improve human life. Mobile telematics is one such development that encompasses multidisciplinary fields for transportation safety. The application of mobile telematics has been explored in many areas, such as insurance and road safety. However, to the best of our knowledge, its application in gender detection has not been explored. This paper proposes a Choquet fuzzy integral vertical bagging classifier that detects gender through mobile telematics. In this model, different random forest classifiers are trained by randomly generated features with rough set theory, and the top three classifiers are fused using the Choquet fuzzy integral. The model is implemented and evaluated on a real dataset. The empirical results indicate that the Choquet fuzzy integral vertical bagging classifier outperforms other classifiers.

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http://hdl.handle.net/10453/137398