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Data Mining and Knowledge Discovery

, Volume 29, Issue 3, pp 792–819 | Cite as

Multi-period classification: learning sequent classes from temporal domains

  • Rui HenriquesEmail author
  • Sara C. Madeira
  • Cláudia Antunes
Article
  • 401 Downloads

Abstract

As the majority of real-world decisions change over time, extending traditional classifiers to deal with the problem of classifying an attribute of interest across different time periods becomes increasingly important. Tackling this problem, referred to as multi-period classification, is critical to answer real-world tasks, such as the prediction of upcoming healthcare needs or administrative planning tasks. In this context, although existing research provides principles for learning single labels from complex data domains, less attention has been given to the problem of learning sequences of classes (symbolic time series). This work motivates the need for multi-period classifiers, and proposes a method, cluster-based multi-period classification (CMPC), that preserves local dependencies across the periods under classification. Evaluation against real-world datasets provides evidence of the relevance of multi-period classifiers, and shows the superior performance of the CMPC method against peer methods adapted from long-term prediction for multi-period tasks with a high number of periods.

Keywords

Multi-period classification Long-term prediction Time-sensitive supervised learning 

Notes

Acknowledgments

The authors deeply thank the reviewers of this manuscript for the detailed, attentive and insightful feedback. This work was supported by Fundação para a Ciência e Tecnologia under the multi-annual funding of INESC-ID PEst-OE/EEI/LA0021/2013 and the Ph.D. Grant SFRH/BD/75924/2011.

References

  1. Aha DW, Kibler D, Albert MK (1991) Instance-based learning algorithms. Mach Learn 6(1):37–66Google Scholar
  2. Azuaje F (2011) Integrative data analysis for biomarker discovery. In: Bioinformatics and biomarker discovery: omic data analysis for personalized medicine, pp 137–154Google Scholar
  3. Bache K, Lichman M (2013) UCI machine learning repositoryGoogle Scholar
  4. Baldi P, Chauvin Y, Hunkapliier Y, McClure M (1994) Hidden Markov models of biological primary sequence information. Proc Natl Acad Sci USA 91(3):1059–1063CrossRefGoogle Scholar
  5. Batista GEAPA, Wang X, Keogh EJ (2011) A complexity-invariant distance measure for time series. In SDM’11. SIAM / Omnipress, Mesa, pp 699–710Google Scholar
  6. Baxter RA, Williams GJ, He H (2001) Feature selection for temporal health records. In PAKDD, London, UK. Springer-Verlag, London, pp 198–209Google Scholar
  7. Ben Taieb S, Bontempi G, Atiya AF, Sorjamaa A (2012) A review and comparison of strategies for multi-step ahead time series forecasting based on the nn5 forecasting competition. Expert Syst Appl 39(8):7067–7083CrossRefGoogle Scholar
  8. Ben Taieb S, Sorjamaa A, Bontempi G (2010) Multiple-output modeling for multi-step-ahead time series forecasting. Neurocomputing 73:1950–1957CrossRefGoogle Scholar
  9. Bengio S, Fessant F, Collobert D (1996) Use of modular architectures for time series prediction. Neural Process Lett 3:101–106CrossRefGoogle Scholar
  10. Bishop C (2006) Pattern recognition and machine learning., Information science and statisticsSpringer, New YorkzbMATHGoogle Scholar
  11. Bontempi G, Ben Taieb S (2011) Conditionally dependent strategies for multiple-step-ahead prediction in local learning. Int J Forecast 27(2004):689–699CrossRefGoogle Scholar
  12. Bontempi G, Birattari M, and Bersini H (1998) Lazy learning for iterated time-series prediction. In Suykens JAK, Vandewalle J (eds) IW on advanced black-box tech for nonlinear modeling, Leuven, Belgium. Katholieke University, Leuven, pp 62–68Google Scholar
  13. Bradley PS, Reina CA, Fayyad UM (2000) Clustering very large databases using EM mixture models. In: Pattern recognition, international conference on 2:2076+Google Scholar
  14. Brahim-Belhouari S, Bermak A (2004) Gaussian process for nonstationary time series prediction. Comput Stat Data Anal 47(4):705–712CrossRefzbMATHMathSciNetGoogle Scholar
  15. Cadez I, Heckerman D, Meek C, Smyth P, White S (2000) Visualization of navigation patterns on a web site using model-based clustering. In: Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’00, New York, NY, USA. ACM, New York, pp 280–284Google Scholar
  16. Ding H, Trajcevski G, Scheuermann P, Wang X, Keogh EJ (2008) Querying and mining of time series data: experimental comparison of representations and distance measures. Proc VLDB Endow 1(2):1542–1552CrossRefGoogle Scholar
  17. Geurts P (2001) Pattern extraction for time series classification. In: Principles of data mining and knowledge discovery. LNCS, vol 2168. Springer, Heidelberg, pp 115–127Google Scholar
  18. Graves A (2012) Supervised sequence labelling with recurrent neural networks., Studies in computational intelligenceSpringer, New YorkCrossRefzbMATHGoogle Scholar
  19. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The weka data mining software: an update. SIGKDD Explor Newsl 11(1):10–18CrossRefGoogle Scholar
  20. Hartigan JA, Wong MA (1979) A k-means clustering algorithm. JSTOR Appl Stat 28(1):100–108CrossRefzbMATHGoogle Scholar
  21. Henriques R, Antunes C (2012) On the need of new approaches for the novel problem of long-term prediction over multi-dimensional data. In: Lee R (ed) Computer and information science 2012, vol 429., Studies in computational intelligenceSpringer, Berlin, pp 121–138CrossRefGoogle Scholar
  22. Henriques R, Antunes C (2014) Learning predictive models from integrated healthcare data: capturing temporal and cross-attribute dependencies. In: HICSS, IEEEGoogle Scholar
  23. Henriques R, Pina S, Antunes C (2013) Temporal mining of integrated healthcare data: methods, revealings and implications. In: SDM IW on data mining for medicine and healthcare. SIAM, pp 52–60Google Scholar
  24. Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264–323CrossRefGoogle Scholar
  25. Ji Y, Hao J, Reyhani N, Lendasse A (2005) Direct and recursive prediction of time series using mutual information selection. In: IWANN. LNCS, vol 3512. Springer, Heidelberg, pp 1010–1017Google Scholar
  26. Kirshner S (2005) Modeling of multivariate time series using hidden Markov models. PhD thesis, AAI3164062Google Scholar
  27. Kriegel H-P, Kröger P, Sander J, Zimek A (2011) Density-based clustering. Wiley Interdisc Rew 1(3):231–240Google Scholar
  28. Letham B, Rudin C, Madigan D (2013) Sequential event prediction. Mach Learn 93(2–3):357–380CrossRefzbMATHMathSciNetGoogle Scholar
  29. Lockett AJ, Miikkulainen R (2009) Temporal convolution machines for sequence learning. Technical report AI-09-04, University of Texas at AustinGoogle Scholar
  30. Mantaci S, Restivo A, Sciortino M (2008) Distance measures for biological sequences: some recent approaches. Int J Approx Reason 47(1):109–124CrossRefzbMATHMathSciNetGoogle Scholar
  31. Moen P (2000) Attribute, event sequence and event type similarity notions for data mining. University of HelsinkiGoogle Scholar
  32. Mörchen F (2003) Time series feature extraction for data mining using DWT and DFT. Reihe Informatik UnivGoogle Scholar
  33. Mörchen F (2006) Time series knowledge mining. Wissenschaft in Dissertationen. Görich & WeiershäuserGoogle Scholar
  34. Murphy K (2002) Dynamic Bayesian networks: representation, inference and learning. PhD thesis, UC Berkeley, Computer Science DivisionGoogle Scholar
  35. Nguyen H-L, Ng W-K, Woon Y-K (2013) Closed motifs for streaming time series classification. KAIS, pp 1–25Google Scholar
  36. Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann Publishers Inc., San Francisco, CAGoogle Scholar
  37. Povinelli RJ, Johnson MT, Lindgren AC, Ye J (2004) Time series classification using gaussian mixture models of reconstructed phase spaces. IEEE Trans Knowl Data Eng 16(6):779–789CrossRefGoogle Scholar
  38. Quinlan R (1993) C4.5: programs for machine learning. Morgan Kaufmann Publishers, San Mateo, CAGoogle Scholar
  39. Rahman S, Bakar A, Hussein Z (2008) A review on protein sequence clustering research. ICBE, vol 21., IFMBE ProceedingsSpringer, Berlin-Heidelberg, pp 275–278Google Scholar
  40. Roddick JF, Spiliopoulou M (2002) A survey of temporal knowledge discovery paradigms and methods. IEEE Trans Knowl Data Eng 14(4):750–767Google Scholar
  41. Sorjamaa A, Hao J, Reyhani N, Ji Y, Lendasse A (2007) Methodology for long-term prediction of time series. Neurocomputing 70:2861–2869CrossRefGoogle Scholar
  42. Sorjamaa A, Lendasse A (2006) Time series prediction using dirrec strategy. In: ESANN’06, pp 143–148Google Scholar
  43. Taieb SB, Bontempi G, Sorjamaa A, Lendasse A (2009) Long-term prediction of time series by combining direct and mimo strategies. In IJCNN, Piscataway, NJ, USA. IEEE Press, pp 1559–1566Google Scholar
  44. Toft P, Rostrup E, Nielsen FA, Nielsen FA, Hansen LK, Goutte C, Goutte C (1998) On clustering fMRI time series. Neuroimage 9:298–310Google Scholar
  45. Tseng V, Lee C-H (2009a) Effective temporal data classification by integrating sequential pattern mining and probabilistic induction. Expert Syst Appl 36(5):9524–9532CrossRefGoogle Scholar
  46. Tseng VS, Lee C-H (2009b) Effective temporal data classification by integrating sequential pattern mining and probabilistic induction. Expert Syst Appl 36(5):9524–9532CrossRefGoogle Scholar
  47. Tsoumakas G, Katakis I (2007) Multi label classification: an overview. Int J Data Wareh Min 3(3):1–13CrossRefGoogle Scholar
  48. Ward JH (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58(301):236–244CrossRefGoogle Scholar
  49. Xi X, Keogh E, Shelton C, Wei L, Ratanamahatana CA (2006) Fast time series classification using numerosity reduction. In ICML. ACM, New York, pp 1033–1040Google Scholar
  50. Zhang M-L, Zhou Z-H (2005) A k-nearest neighbor based algorithm for multi-label classification. IEEE International Conference on Granular Computing, vol 2, pp 718–721Google Scholar

Copyright information

© The Author(s) 2014

Authors and Affiliations

  • Rui Henriques
    • 1
    Email author
  • Sara C. Madeira
    • 1
  • Cláudia Antunes
    • 1
  1. 1.KDBIO group, INESC-ID, Instituto Superior Técnico, Universidade de Lisboa Computer Science and Engineering DepartmentInstituto Superior Técnico, Universidade de LisboaLisboaPortugal

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