A comparative study of sampling methods and algorithms for imbalanced time series classification

Liang, G; Zhang, C

A comparative study of sampling methods and algorithms for imbalanced time series classification

Liang, G

Zhang, C

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, 7691 LNAI pp. 637 - 648
Issue Date:: 2012-12-26

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Field	Value	Language
dc.contributor.author	Liang, G https://orcid.org/0000-0002-6843-7431	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2012-12-26	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, 7691 LNAI pp. 637 - 648	en_US
dc.identifier.isbn	9783642351006	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22243
dc.identifier.uri	http://hdl.handle.net/10453/32305
dc.description.abstract	Mining time series data and imbalanced data are two of ten challenging problems in data mining research. Imbalanced time series classification (ITSC) involves these two challenging problems, which take place in many real world applications. In the existing research, the structure-preserving over-sampling (SOP) method has been proposed for solving the ITSC problems. It is claimed by its authors to achieve better performance than other over-sampling and state-of-the-art methods in time series classification (TSC). However, it is unclear whether an under-sampling method with various learning algorithms is more effective than over-sampling methods, e.g., SPO for ITSC, because research has shown that under-sampling methods are more effective and efficient than over-sampling methods. We propose a comparative study between an under-sampling method with various learning algorithms and over-sampling methods, e.g. SPO. Statistical tests, the Friedman test and post-hoc test are applied to determine whether there is a statistically significant difference between methods. The experimental results demonstrate that the under-sampling technique with KNN is the most effective method and can achieve results that are superior to the existing complicated SPO method for ITSC. © 2012 Springer-Verlag.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-35101-3_54	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A comparative study of sampling methods and algorithms for imbalanced time series classification	en_US
dc.type	Conference Proceeding
utslib.citation.volume	7691 LNAI	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Sydney	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	7691 LNAI	en_US

Abstract:

Mining time series data and imbalanced data are two of ten challenging problems in data mining research. Imbalanced time series classification (ITSC) involves these two challenging problems, which take place in many real world applications. In the existing research, the structure-preserving over-sampling (SOP) method has been proposed for solving the ITSC problems. It is claimed by its authors to achieve better performance than other over-sampling and state-of-the-art methods in time series classification (TSC). However, it is unclear whether an under-sampling method with various learning algorithms is more effective than over-sampling methods, e.g., SPO for ITSC, because research has shown that under-sampling methods are more effective and efficient than over-sampling methods. We propose a comparative study between an under-sampling method with various learning algorithms and over-sampling methods, e.g. SPO. Statistical tests, the Friedman test and post-hoc test are applied to determine whether there is a statistically significant difference between methods. The experimental results demonstrate that the under-sampling technique with KNN is the most effective method and can achieve results that are superior to the existing complicated SPO method for ITSC. © 2012 Springer-Verlag.

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http://hdl.handle.net/10453/22243