Adaptive Subspace Sampling for Class Imbalance Processing-Some clarifications, algorithm, and further investigation including applications to Brain Computer Interface

Lin, CT; Huang, KC; Pal, NR; Cao, Z; Liu, YT; Fang, CN; Hsieh, TY; Lin, YY; Wu, SL

Adaptive Subspace Sampling for Class Imbalance Processing-Some clarifications, algorithm, and further investigation including applications to Brain Computer Interface

Lin, CT Huang, KC Pal, NR Cao, Z

Liu, YT Fang, CN Hsieh, TY Lin, YY Wu, SL

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2020 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2020, 2020, 00, pp. 1-8
Issue Date:: 2020-11-04

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Field	Value	Language
dc.contributor.author	Lin, CT
dc.contributor.author	Huang, KC
dc.contributor.author	Pal, NR
dc.contributor.author	Cao, Z https://orcid.org/0000-0003-3656-0328
dc.contributor.author	Liu, YT
dc.contributor.author	Fang, CN
dc.contributor.author	Hsieh, TY
dc.contributor.author	Lin, YY
dc.contributor.author	Wu, SL
dc.date	2020-11-04
dc.date.accessioned	2021-03-16T00:02:53Z
dc.date.available	2021-03-16T00:02:53Z
dc.date.issued	2020-11-04
dc.identifier.citation	2020 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2020, 2020, 00, pp. 1-8
dc.identifier.isbn	9781728198125
dc.identifier.uri	http://hdl.handle.net/10453/147200
dc.description.abstract	© 2020 IEEE. Kohonen's Adaptive Subspace Self-Organizing Map (ASSOM) learns several subspaces of the data where each subspace represents some invariant characteristics of the data. To deal with the imbalance classification problem, earlier we have proposed a method for oversampling the minority class using Kohonen's ASSOM. This investigation extends that study, clarifies some issues related to our earlier work, provides the algorithm for generation of the oversamples, applies the method on several benchmark data sets, and makes an application to a Brain Computer Interface (BCI) problem. First we compare the performance of our method using some benchmark data sets with several state-of-The-Art methods. Finally, we apply the ASSOM-based technique to analyze a BCI based application using electroencephalogram (EEG) datasets. Our results demonstrate the effectiveness of the ASSOM-based method in dealing with imbalance classification problem.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2020 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2020
dc.relation.ispartof	2020 International Conference on Fuzzy Theory and Its Applications (iFUZZY)
dc.relation.isbasedon	10.1109/iFUZZY50310.2020.9297814
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Adaptive Subspace Sampling for Class Imbalance Processing-Some clarifications, algorithm, and further investigation including applications to Brain Computer Interface
dc.type	Conference Proceeding
utslib.citation.volume	00
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/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2021-03-16T00:02:52Z
pubs.finish-date	2020-11-07
pubs.publication-status	Published
pubs.start-date	2020-11-04
pubs.volume	00

Abstract:

© 2020 IEEE. Kohonen's Adaptive Subspace Self-Organizing Map (ASSOM) learns several subspaces of the data where each subspace represents some invariant characteristics of the data. To deal with the imbalance classification problem, earlier we have proposed a method for oversampling the minority class using Kohonen's ASSOM. This investigation extends that study, clarifies some issues related to our earlier work, provides the algorithm for generation of the oversamples, applies the method on several benchmark data sets, and makes an application to a Brain Computer Interface (BCI) problem. First we compare the performance of our method using some benchmark data sets with several state-of-The-Art methods. Finally, we apply the ASSOM-based technique to analyze a BCI based application using electroencephalogram (EEG) datasets. Our results demonstrate the effectiveness of the ASSOM-based method in dealing with imbalance classification problem.

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http://hdl.handle.net/10453/147200