Part of the book series:Lecture Notes in Computer Science ((LNISA,volume 8644))
Included in the following conference series:
1338Accesses
Abstract
Multivariate time series (MTS) classification is an important topic in time series data mining, and has attracted great interest in recent years. However, early classification on MTS data largely remains a challenging problem. To address this problem, we focus on discovering hidden knowledge from the data for early classification in an explainable way. At first, we introduce a method MCFEC (Mining Core Feature for Early Classification) to obtain distinctive and early shapelets as core features of each variable independently. Then, two methods are introduced for early classification on MTS based on core features. Experimental results on both synthetic and real-world datasets clearly show that our proposed methods can achieve effective early classification on MTS.
This is a preview of subscription content,log in via an institution to check access.
Access this chapter
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
- Chapter
- JPY 3498
- Price includes VAT (Japan)
- eBook
- JPY 5719
- Price includes VAT (Japan)
- Softcover Book
- JPY 7149
- Price includes VAT (Japan)
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Batal, I., Sacchi, L., Bellazzi, R., Hauskrecht, M.: Multivariate time series classification with temporal abstractions. In: Proceedings of the Twenty-Second International FLAIRs Conference (2009)
Batal, I., Fradkin, D., Harrison, J., et al.: Mining recent temporal patterns for event detection in multivatiate time series data. In: KDD 2012 (2012)
Orsenigo, C., Vercellis, C.: Combining discrete SVM and fixed cardinality warping distances for multivariate time series classification. Pattern Recognition 43, 3787–3794 (2010)
Chandrakala, S., Chandra Sekhar, C.: Classification of varying length multivariate time series using Gaussian mixture models and support vector machines. Int. J. of Data Mining, Modeling and Management 2(3), 268–287 (2010)
Yoon, H., Yang, K., Shahabi, C.: Feature subset selection and feature ranking for multivariate time series. IEEE Transactions on Knowledge and Data Engineering 17(9), 1186–1198 (2005)
Yang, K., Yoon, H., Shahabi, C.:CLeVer: A Feature Subset Selection Technique for Multivariate Time Series. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 516–522. Springer, Heidelberg (2005)
Kadous, M.W., Sammut, C.: Classification of multivariate time series and structured data using constructive induction. Machine Learning 58, 179–216 (2005)
Xing, Z., Pei, J., Yu, P.S., Wang, K.: Extracting interpretable features for early classification on time series. In: SDM 2011 (2011)
Xing, Z., Pei, J., Yu, P.S.: Early prediction on time series: a nearest neighbor approach. In: IJCAI (2009)
Ghalwash, M.F., Obradovic, Z.: Early classification of multivariate temporal observations by extraction of interpretable shapelets. BMC Bioinformatics (August 2012)
Lines, J., Davis, L.M., Hills, J., Bagnall, A.: A shapelet transform for time series classification. In: KDD 2012 (2012)
Wei, L., Keogh, E.: Semi-supervised time series classification. In: KDD 2006 (2006)
Nguyen, M.N., Li, X.-L., Ng, S.-K.: Positive unlabeled learning for time series classification. IJCAI 2011 (2011)
Xi, X., Keogh, E., Shelton, C., Wei, L.: Fast time series classification using numerosity reduction. In: ICML 2006 (2006)
Jeong, Y.-S., Jeong, M.K., Omitaomu, O.A.: Weighted dynamic time warping for time series classification. Pattern Recognition 44, 2231–2240 (2011)
Ye, L., Keogh, E.: Time series shapelets: a new primitive for data mining. In: KDD 2009 (2009)
Sun, Y., Kamel, M.S., Wong, A.K.C., Wang, Y.: Cost-sensitive boosting for classification of imbalanced data. Pattern Recognition 40, 3358–3378 (2007)
Rousseeuw, P.J.: Silhouettes: a Graphical Aid to the Interpretation and Validation of Cluster Analysis. Computational and Applied Mathematics 20, 53–65 (1987)
He, G., Duan, Y., et al.: Early Prediction on Imbalanced Multivariate Time Series. In: CIKM 2013 (2013)
Seung, H.S., Opper, M., Sompolinsky, H.: Query by committee. In: COLT 1992 Proceedings of the Fifth Annual Workshop on Computational Learning Theory,, pp. 287–294 (1992)
Author information
Authors and Affiliations
State Key Laboratory of Software Engineering, Wuhan University, China
Guoliang He, Yong Duan, Guofu Zhou & Lingling Wang
College of Computer Science, Wuhan University, China
Guoliang He, Yong Duan, Guofu Zhou & Lingling Wang
College of Information, Huazhong Agricultural University, China
Lingling Wang
- Guoliang He
You can also search for this author inPubMed Google Scholar
- Yong Duan
You can also search for this author inPubMed Google Scholar
- Guofu Zhou
You can also search for this author inPubMed Google Scholar
- Lingling Wang
You can also search for this author inPubMed Google Scholar
Editor information
Editors and Affiliations
Instituto Tecnológico de Informática, 46022, Valencia, Spain
Hendrik Decker
Faculty of Electrical Engineering, Department of Cybernetics, Czech Technical University in Prague, 166 27, Prague 6, Czech Republic
Lenka Lhotská
Department of Computer Science, The University of Auckland, 1010, Auckland, New Zealand
Sebastian Link
Knowledge Management, LMU University of Munich, Leopoldstraße 13, 80802, Munich, Germany
Marcus Spies
University of Linz, FAW, Altenbergerstrasse 69,, 4040, Linz, Austria
Roland R. Wagner
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
He, G., Duan, Y., Zhou, G., Wang, L. (2014). Early Classification on Multivariate Time Series with Core Features. In: Decker, H., Lhotská, L., Link, S., Spies, M., Wagner, R.R. (eds) Database and Expert Systems Applications. DEXA 2014. Lecture Notes in Computer Science, vol 8644. Springer, Cham. https://doi.org/10.1007/978-3-319-10073-9_35
Download citation
Publisher Name:Springer, Cham
Print ISBN:978-3-319-10072-2
Online ISBN:978-3-319-10073-9
eBook Packages:Computer ScienceComputer Science (R0)
Share this paper
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative