Skip to main content
SpringerLink
Log in

Confidence-based early classification of multivariate time series with multiple interpretable rules

  • Theoretical advances
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

In the process of early classification, earliness and accuracy are two key indicators to evaluate the performance of classification, and early classification usually weaken its accuracy to some degree. Therefore, how to find a tradeoff between two conflict objectives is a challenging work. So far, there are just a few work touched the quality of early classification on univariate time series, and the confidence estimation for early classification on multivariate time series (MTS) is still an open issue. In this paper, we focus on interpretably classifying MTS examples as early as possible while guaranteeing the quality of the classification results. First, a fast method is proposed to mine interpretable and local rules from the MTS training data. Second, a valid measure is advanced to estimate the confidence of early classification on MTS examples. Finally, a strategy is designed to execute confident early classification to assume the classification confidence meets customers’ requirement. Experiment results on seven datasets show that the effectiveness and efficiency of our proposed algorithm for confident early classification on multivariate time series.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Mori U, Mendiburu A, Dasgupta S, Lozano JA (2017) Early classification of time series by simultaneously optimizing the accuracy and earliness. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/tnnls.2017.2764939

    Article  Google Scholar 

  2. He G, Duan Y, Peng R, Jing X, Qian T, Wang L (2015) Early classification on multivariate time series. Neurocomputing 149:777–787

    Article  Google Scholar 

  3. He G, Li Y, Zhao W (2017) An uncertainty and density based active semi-supervised learning scheme for positive unlabeled multivariate time series classification. Knowl Based Syst 124:80–92

    Article  Google Scholar 

  4. Milo MW, Roan MJ (2017) Detecting anomalous patterns in time-series data using sparse hierarchically parameterized transition matrices. Pattern Anal Appl 20(4):1029–1043

    Article  MathSciNet  Google Scholar 

  5. Li Y, Zhai Q, Ding S, Yang F, Li G, Zheng YF (2017) Efficient health-related abnormal behavior detection with visual and inertial sensor integration. Pattern Anal Appl. https://doi.org/10.1007/s10044-017-0660-5

    Article  Google Scholar 

  6. Zhang C, Adeli E, Zhou T, Chen X, Shena D (2018) Multi-layer multi-view classification for Alzheimer’s disease diagnosis. In: AAAI

  7. Aburomman AA, Reaz MBI (2017) A survey of intrusion detection systems based on ensemble and hybrid classifiers. Comput Secur 65:135–152

    Article  Google Scholar 

  8. Ghalwash MF, Radossavljevic V, Obradovic Z (2014) Utilizing temporal patterns for estimating uncertainty in interpretable early decision making. In: KDD

  9. Mori U, Mendiburu A, Keogh E, Lozano JA (2017) Reliable early classification of time series based on discriminating the classes over time. Data Min Knowl Disc 31(1):233–263

    Article  MathSciNet  Google Scholar 

  10. Gong Z, Chen H, Yao X (2018) Multiobjective learning in the model space for time series classification. IEEE Trans Cybern 1:8. https://doi.org/10.1109/tcyb.2018.2789422

    Article  Google Scholar 

  11. Sharabiani A, Darabi H, Rezaei A, Harford S, Johnson H, Karim F (2017) Efficient classification of long time series by 3-D dynamic time warping. IEEE Trans Syst Man Cybern Syst 47(10):2688–2703

    Article  Google Scholar 

  12. Han M, Zhang R, Qiu T, Xu M, Ren W (2017) Multivariate chaotic time series prediction based on improved grey relational analysis. IEEE Trans Syst Man Cybern Syst. https://doi.org/10.1109/tsmc.2017.2758579

    Article  Google Scholar 

  13. Han M, Zhang S, Xu M, Qiu T, Wang N (2018) Multivariate chaotic time series online prediction based on improved kernel recursive least squares algorithm. IEEE Trans Cybern. https://doi.org/10.1109/tcyb.2018.2789686

    Article  Google Scholar 

  14. Hallac D, Vare S, Boyd S, Leskovec J (2017) Toeplitz inverse covariance-based clustering of multivariate time series data. In: KDD

  15. He G, Chen L, Zeng C, Zheng Q, Zhou G (2016) Probabilistic skyline queries on uncertain time series. Neurocomputing 191:224–237

    Article  Google Scholar 

  16. Linardi M, Zhu Y, Palpanas T, Keogh E (2018) Matrix profile X: VALMOD—scalable discovery of variable-length motifs in data series. In: SIGMOD

  17. Yao L, Su L, Li Q, Li Y, Ma Y, Gao J, Zhang A (2018) Online truth discovery on time series data. In: SDM

  18. Baek S, Kim DY (2017) Empirical sensitivity analysis of discretization parameters for fault pattern extraction from multivariate time series data. IEEE Trans Cybern 47(5):1198–1209

    Article  Google Scholar 

  19. Batal I, Cooper GF, Fradkin D, Harrison J Jr, Moerchen F, Hauskrecht M (2016) An efficient pattern mining approach for event detection in multivariate temporal data. Knowl Inf Syst 46:115–150

    Article  Google Scholar 

  20. Hu B, Chen Y, Zakaria J, Ulanova L, Keogh E (2013) Classification of multi-dimensional streaming time series by weighting each classifier’s track record. In: ICDM

  21. Xing Z, Pei J, Yu PS (2012) Early classification on time series. Knowl Inf Syst 31(1):105–127

    Article  Google Scholar 

  22. Parrish N, Anderson HS, Gupa MR, Hsiao DY (2013) Classifying with confidence from incomplete information. J Mach Learn Res 14:3561–3589

    MathSciNet  MATH  Google Scholar 

  23. Lin YF, Chen HH, Tseng VS, Pei J (2015) Reliable early classification on multivariate time series with numerical and categorical attributes. In: The Pacific-Asia conference on knowledge discovery and data mining, Ho Chi Minh, May 19, 2015–May 22, pp 199–211

  24. Wiens J, Guttag J, Horvitz E (2012) Patient risk stratification for hospital-associated c. diff as a timeseries classification task. In: Proceedings of twenty-sixth annual conference on neural information processing system (NIPS), Lake Tahoe, Nevada, USA, December 2–6, 2012, pp 476–484

  25. Ye L, Keogh E (2011) Time series shapelets: a novel technique that allows accurate, interpretable and fast classification. Data Min Knowl Disc 22:149–182

    Article  MathSciNet  Google Scholar 

  26. He G, Zhao W, Xia X, Peng R, Wu X (2018) An ensemble of shapelet-based classifiers on inter-class and intra-class imbalanced multivariate time series at the early stage. Soft Comput. https://doi.org/10.1007/s00500-018-3261-3

    Article  Google Scholar 

  27. Rodriguez A, Laio A (2014) Clustering by fast search and find of density peaks. Science 344(6191):1492–1496

    Article  Google Scholar 

  28. Begum N, Ulanova L, Wang J, Keogh E (2015) Accelerating dynamic time warping clustering with a novel admissible pruning strategy. In: KDD

  29. Dua D, Karra Taniskidou E (2017) UCI machine learning repository. University of California, School of Information and Computer Science, Irvine. http://archive.ics.uci.edu/ml

  30. http://www.cs.cmu.edu/~bobski/

  31. http://www.cs.ucr.edu/~eamonn/time_series_data/

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (61876136).

Author information

Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, China

    Guoliang He & Wen Zhao

  2. School of Software, East China Jiaotong University, Nanchang, China

    Xuewen Xia

Authors
  1. Guoliang He
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wen Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xuewen Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Guoliang He or Xuewen Xia.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

He, G., Zhao, W. & Xia, X. Confidence-based early classification of multivariate time series with multiple interpretable rules. Pattern Anal Applic 23, 567–580 (2020). https://doi.org/10.1007/s10044-019-00782-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-019-00782-7

Keywords

Search

Navigation