Learning a symbolic representation for multivariate time series classification

Data Mining and Knowledge Discovery

Volumn 29, Issue 2, 2015, Pages 400-422

  Baydogan, Mustafa Gokce ^a   Runger, George ^b  

^a BOGAZICI UNIVERSITY   (Turkey)

^b ARIZONA STATE UNIVERSITY   (United States)

Author keywords

Codebook; Decision trees; Supervised learning

Indexed keywords

DECISION TREES; FEATURE EXTRACTION; LARGE DATASET; SUPERVISED LEARNING; TIME SERIES;

CODEBOOKS; EXTRACT INFORMATIONS; MULTIVARIATE TIME SERIES; MULTIVARIATE TIME SERIES CLASSIFICATIONS; NEAREST NEIGHBOR CLASSIFIER; NUMERICAL ATTRIBUTES; SYMBOLIC REPRESENTATION; UNIVARIATE TIME SERIES;

CLASSIFICATION (OF INFORMATION);

EID: 84923215420     PISSN: 13845810     EISSN: 1573756X     Source Type: Journal    
DOI: 10.1007/s10618-014-0349-y     Document Type: Article

References (39)

1. Akl A, Valaee S (2010) Accelerometer-based gesture recognition via dynamic-time warping, affinity propagation, compressive sensing. In Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on, pp 2270–2273, March
2. Bache K, Lichman M (2013) UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA. http://archive.ics.uci.edu/ml
3. Bankó Z, Abonyi J (2012) Correlation based dynamic time warping of multivariate time series. Expert Systems with Applications 18(5):231–241
4. Baydogan MG (2012) Modeling Time Series Data for Supervised Learning. PhD thesis, Arizona State University, Dec.
5. Baydogan MG (2013) Multivariate time series classification. homepage: www.mustafabaydogan.com/multivariate-time-series-discretization-for-classification.html
6. Baydogan MG, Runger G, Tuv E (2013) A bag-of-features framework to classify time series. Pattern Analysis and Machine Intelligence, IEEE Transactions on 35(11):2796–2802
7. Bicego M, Pekalska E, Tax DMJ, Duin RPW (2009) Component-based discriminative classification for hidden Markov models. Pattern Recognition 42(11):2637–2648
8. Breiman L (2001) Random forests. Machine Learning 45(1):5–32
9. Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and Regression Trees. Wadsworth, Belmont, MA
10. Brodley C, Utgoff P (1995) Multivariate decision trees. Machine Learning 19(1):45–77
11. Chakrabarti K, Keogh E, Mehrotra S, Pazzani M (2002) Locally adaptive dimensionality reduction for indexing large time series databases. ACM Trans. Database Syst. 27(2):188–228
12. Chaovalitwongse W, Pardalos P (2008) On the time series support vector machine using dynamic time warping kernel for brain activity classification. Cybernetics and Systems Analysis 44:125–138
13. Fu T-C (2011) A review on time series data mining. Engineering Applications of Artificial Intelligence 24:164–181
14. Geurts P (2001) Pattern extraction for time series classification. Principles of Data Mining and Knowledge Discovery, volume 2168 of Lecture Notes in Computer ScienceSpringer, Berlin / Heidelberg, pp 115–127
15. Hammami N, Bedda M (2010) Improved tree model for arabic speech recognition. In Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on, volume 5, pages 521–526, July
16. Kadous MW, Sammut C (2005) Classification of multivariate time series and structured data using constructive induction. Machine Learning 58:179–216
17. Keogh E, Zhu Q, Hu B, Y. H, Xi X, Wei L, Ratanamahatana CA (2011) The UCR time series classification/clustering. homepage: www.cs.ucr.edu/~eamonn/time_series_data/
18. Kudo M, Toyama J, Shimbo M (1999) Multidimensional curve classification using passing-through regions. Pattern Recognition Letters 20(1113):1103–1111
19. Kuksa PP (2012) 2d similarity kernels for biological sequence classification. In ACM SIGKDD Workshop on Data Mining in Bioinformatics
20. Li C, Khan L, Prabhakaran B (2006) Real-time classification of variable length multi-attribute motions. Knowledge and Information Systems 10:163–183
21. Li C, Khan L, Prabhakaran B (2007) Feature selection for classification of variable length multiattribute motions. In Multimedia Data Mining and Knowledge Discovery, pages 116–137. Springer London
22. Lin J, Keogh E, Lonardi S, Chiu B (2003) A symbolic representation of time series, with implications for streaming algorithms. In In Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp 2–11. ACM Press
23. Lin J, Keogh E, Wei L, Lonardi S (2007) Experiencing SAX: a novel symbolic representation of time series. Data Mining and Knowledge Discovery 15:107–144
24. Lin J, Khade R, Li Y (2012) Rotation-invariant similarity in time series using bag-of-patterns representation. Journal of Intelligent Information Systems, pp 1–29
25. Lin J, Williamson S, Borne K, DeBarr D (2012) Pattern recognition in time series. In Advances in Machine Learning and Data Mining for Astronomy, Chapman & Hall, To appear.
26. Liu J, Wang Z, Zhong L, Wickramasuriya J, Vasudevan V (2009) uWave: Accelerometer-based personalized gesture recognition and its applications. Pervasive Computing and Communications, IEEE International Conference on 0:1–9
27. McGovern A, Rosendahl D, Brown R, Droegemeier K (2011) Identifying predictive multi-dimensional time series motifs: an application to severe weather prediction. Data Mining and Knowledge Discovery 22:232–258
28. Moosmann F, Nowak E, Jurie F (2008) Randomized clustering forests for image classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 30:1632–1646
29. Olszewski RT (2012) http://www.cs.cmu.edu/~bobski/. accessed: June 10
30. Ordonez P, Armstrong T, Oates T, Fackler J (2011) Using modified multivariate bag-of-words models to classify physiological data. In Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops, ICDMW ’11, pages 534–539, Washington, DC, USA, IEEE Computer Society.
31. Orsenigo C, Vercellis C (2010) Combining discrete svm and fixed cardinality warping distances for multivariate time series classification. Pattern Recognition 43(11):3787–3794
32. Quinlan JR (1993) C4.5: Programs for Machine Learning. Morgan Kaufmann,
33. Ratanamahatana C, Keogh E (2004) Making time-series classification more accurate using learned constraints. In Proceedings of SIAM International Conference on Data Mining (SDM04), pp 11–22
34. Ratanamahatana C, Keogh E (2005) Three myths about dynamic time warping data mining. In Proceedings of SIAM International Conference on Data Mining (SDM05), volume 21, pp 506–510
35. Sakoe H (1978) Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing 26:43–49
36. Schaefer JT (1990) The Critical Success Index as an Indicator of Warning Skill. Weather and Forecasting 5(4):570–575
37. Shieh J, Keogh E (2008) isax: indexing and mining terabyte sized time series. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD ’08, pages 623–631, New York, NY, USA, ACM.
38. CMU Graphics Lab Motion Capture Database. Homepage: mocap.cs.cmu.edu, 2012
39. Weng X, Shen J (2008) Classification of multivariate time series using locality preserving projections. Knowledge-Based Systems 21(7):581–587