Time-series classification based on individualised error prediction

Proceedings - 2010 13th IEEE International Conference on Computational Science and Engineering, CSE 2010

Volumn , Issue , 2010, Pages 48-54

  Buza, Krisztian ^a   Nanopoulos, Alexandros ^a   Schmidt Thieme, Lars ^a  

^a UNIVERSITY OF HILDESHEIM   (Germany)

Author keywords

Classification; Error estimation; Time series

Indexed keywords

CLASSIFICATION; CLASSIFICATION ERRORS; DATA SETS; DISCRETE TIME; ERROR ESTIMATIONS; ERROR PREDICTION; EXPERIMENTAL EVALUATION; FINE GRAINED; K-NN CLASSIFIER; K-VALUES; MACHINE-LEARNING; REAL TIME; REGRESSION MODEL; RESEARCH TOPICS; SINGLE-VALUE; STATE OF THE ART; TIME SERIES CLASSIFICATIONS; TIME-SERIES DATA;

CLASSIFIERS; REGRESSION ANALYSIS; TEXT PROCESSING; TIME SERIES;

FORECASTING;

EID: 79951628123     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CSE.2010.16     Document Type: Conference Paper

References (24)

1. K. Buza, L. Schmidt-Thieme, Motif-based Classification of Time Series with Bayesian Networks and SVMs, Proc. of 32nd Annual Conference of the Gesellschaft für Klassifikation (GfKl 2008), Springer Verlag, 2009.
2. H. Ding, G. Trajcevski, P. Scheuermann, X. Wang, E. Keogh, Querying and Mining of Time Series Data: Experimental Comparison of Representations and Distance Measures, VLDB'08, 2008.
3. C. Domeniconi and D. Gunopulos, Adaptive Nearest Neighbor Classification using Support Vector Machines, NIPS, 2001.
4. C. Domeniconi, J. Peng, D. Gunopulos, Locally Adaptive Metric Nearest-Neighbor Classification, Transactions on Pattern Analysis and Machine Intelligence, 2002.
5. N. Duffy, D. Helmbold, Boosting Methods for Regression, Machine Learning, 47, pp. 153-200, 2002. (Pubitemid 34126109)
6. D. Eads, D. Hill, S. Davis, S. Perkins, J. Ma, R. Porter and J. Theiler, Genetic algorithms and suppor vector machines for time series classification, In Proc. of the International Society for Optical Engineering (SPIE), volume 4787, pages 74-85, 2002.
7. P. Geurts Pattern Extraction for Time Series Classification, PKDD, LNAI 2168, pp. 115-127, Springer Verlag, 2001. (Pubitemid 33331855)
8. T. Hastie and R. Tibshirani, Discriminant Adaptive Nearest Neighbor Classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 18, No. 6, 1996.
9. T. Hastie, R. Tibshirani, J. H. Friedman, The Elements of Statistical Learning Springer, 2009.
10. A. K. Jain, R. C. Dubes, C.-C. Chen, Bootstrap Techniques for Error Estimation, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-9, no. 5, 1987.
11. E. Keogh, S. Kasetty, On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration SIGKDD, 2002.
12. E.J. Keogh, M.J. Pazzani. Scaling up Dynamic Time Warping for Datamining Applications. KDD, pages 285 - 289, Boston, Massachusetts, USA, 2000.
13. E. Keogh, C. Shelton, and F. Moerchen, Workshop and Challenge on Time Series Classification, Int. Conf. on Knowledge Discovery and Data Mining (KDD), 2007, http://www.cs.ucr.edu/̃eamonn/SIGKDD2007TimeSeries.html.
14. A. M. Molinaro, R. Simon, R. M. Pfeiffer, Prediction error estimation: a comparison of resampling methods, Bioinformatics, Vol. 21. no. 15, pp. 3301-3307, 2005. (Pubitemid 41418445)
15. S. Ougiaroglou, A. Nanopoulos, A. N. Papadopoulos, Y. Manolopoulos, T. Welzer-Druzovec, Adaptive k-Nearest- Neighbor Classification Using a Dynamic Number of Nearest Neighbors, Advances in Databases and Information Systems, LNCS Vol. 4690/2007, Springer-Verlag, 2007.
16. V. Petridis and A. Kehagias, Predictive modular neural networks for time series classification, Neural Networks, 10(1):31-49, 1997. (Pubitemid 27086444)
17. M. Radovanovic and A. Nanopoulos and M. Ivanovic, Time-Series Classification in Many Intrinsic Dimensions, 10th SIAM International Conference on Data Mining.
18. C.A. Ratanamahatana, E. Keogh. Everything you Know about Dynamic Time Warping is Wrong. SIGKDD W'shop on Mining Temporal and Seq. Data, 2004.
19. C.A. Ratanamahatana, E. Keogh. Making Time-series Classification More Accurate Using Learned Constraints. SIAM Int'l Conf. on Data Mining, 2004.
20. T.M. Rath, R. Manmatha. Word image matching using dynamic time wrapping. CVPR, II:521-527, 2003.
21. H. Sakoe, S. Chiba. Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoustics, Speech, and Signal Proc., ASSP-26:43-49, 1978. (Pubitemid 8601900)
22. P. Sykacek and S. Roberts Bayesian time series classification, Advances in Neural Processing Systems 14.
23. K. Tsuda, G. R̈atsch, S. Mika, K.-R. Müller, Learning to Predict the Leave-one-out Error of Kernel Based Classifiers, Artificial Neural Networks, ICANN 2001, LNCS Vol. 2130/2001, Springer Verlag, 2001.
24. D. Wettschereck, T. Dietterich, Locally Adaptive Nearest Neighbor Algorithms, Advances in Neural Information Processing Systems 6, Morgan Kaufmann Publishers, San Mateo, 1994.