- Title
- Enhancing linear time complexity time series classification with hybrid bag-of-patterns
- Creator
- Liang, Shen; Zhang, Yanchun; Ma, Jiangang
- Date
- 2020
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/174065
- Identifier
- vital:14778
- Identifier
-
https://doi.org/10.1007/978-3-030-59410-7_50
- Identifier
- ISBN:0302-9743 (ISSN); 9783030594091 (ISBN)
- Abstract
- In time series classification, one of the most popular models is Bag-Of-Patterns (BOP). Most BOP methods run in super-linear time. A recent work proposed a linear time BOP model, yet it has limited accuracy. In this work, we present Hybrid Bag-Of-Patterns (HBOP), which can greatly enhance accuracy while maintaining linear complexity. Concretely, we first propose a novel time series discretization method called SLA, which can retain more information than the classic SAX. We use a hybrid of SLA and SAX to expressively and compactly represent subsequences, which is our most important design feature. Moreover, we develop an efficient time series transformation method that is key to achieving linear complexity. We also propose a novel X-means clustering subroutine to handle subclasses. Extensive experiments on over 100 datasets demonstrate the effectiveness and efficiency of our method. © 2020, Springer Nature Switzerland AG.
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Relation
- 25th International Conference on Database Systems for Advanced Applications, DASFAA 2020 Vol. 12112 LNCS, p. 717-735
- Rights
- Copyright © 2020 Springer Nature Switzerland AG.
- Rights
- This metadata is freely available under a CCO license
- Subject
- Bag-Of-Patterns; Classification; Time series
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