Using AdaBoost-based Multiple Functional Neural Fuzzy Classifiers Fusion for Classification Applications

Jhang, JY; Lee, CL; Lin, CJ; Lin, CT; Young, KY

Using AdaBoost-based Multiple Functional Neural Fuzzy Classifiers Fusion for Classification Applications

Jhang, JY Lee, CL Lin, CJ Lin, CT

Young, KY

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Publication Type:: Conference Proceeding
Citation:: MATEC Web of Conferences, 2018, 201
Issue Date:: 2018-09-14

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Field	Value	Language
dc.contributor.author	Jhang, JY	en_US
dc.contributor.author	Lee, CL	en_US
dc.contributor.author	Lin, CJ	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.contributor.author	Young, KY	en_US
dc.date.issued	2018-09-14	en_US
dc.identifier.citation	MATEC Web of Conferences, 2018, 201	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130046
dc.description.abstract	© The Authors, published by EDP Sciences, 2018. In this study, two intelligent classifiers, the AdaBoost-based incremental functional neural fuzzy classifier (AIFNFC) and the AdaBoost-based fixed functional neural fuzzy classifier (AFFNFC), are proposed for solving the classification problems. The AIFNFC approach will increase the amount of functional neural fuzzy classifiers based on the corresponding error during the training phase; while the AFNFC approach is equipped with a fixed amount of functional neural fuzzy classifiers. Then, the weights of AdaBoost procedure are assigned for classifiers. The proposed methods are applied to different classification benchmarks. Results of this study demonstrate the effectiveness of the proposed AIFNFC and AFFNFC methods.	en_US
dc.relation.ispartof	MATEC Web of Conferences	en_US
dc.relation.isbasedon	10.1051/matecconf/201820105004	en_US
dc.title	Using AdaBoost-based Multiple Functional Neural Fuzzy Classifiers Fusion for Classification Applications	en_US
dc.type	Conference Proceeding
utslib.citation.volume	201	en_US
utslib.for	0802 Computation Theory and Mathematics	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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	201	en_US

Abstract:

© The Authors, published by EDP Sciences, 2018. In this study, two intelligent classifiers, the AdaBoost-based incremental functional neural fuzzy classifier (AIFNFC) and the AdaBoost-based fixed functional neural fuzzy classifier (AFFNFC), are proposed for solving the classification problems. The AIFNFC approach will increase the amount of functional neural fuzzy classifiers based on the corresponding error during the training phase; while the AFNFC approach is equipped with a fixed amount of functional neural fuzzy classifiers. Then, the weights of AdaBoost procedure are assigned for classifiers. The proposed methods are applied to different classification benchmarks. Results of this study demonstrate the effectiveness of the proposed AIFNFC and AFFNFC methods.

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http://hdl.handle.net/10453/130046