Feature selection for change detection in multivariate time-series

Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007

Volumn , Issue , 2007, Pages 590-597

  Botsch, Michael ^a   Nossek, Josef A ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); ERROR ANALYSIS; LEARNING SYSTEMS; MULTIVARIABLE CONTROL SYSTEMS; TIME SERIES ANALYSIS;

CHANGE DETECTION; CRASH DETECTION; MULTIVARIATE TIME-SERIES;

FEATURE EXTRACTION;

EID: 34548782859     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIDM.2007.368929     Document Type: Conference Paper

References (27)

1. G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecasting and Control, Prentice Hall, 1994.
2. R. Bellman, Adaptive Control Processes: A Guided Tour, New Jersey: Princeton University Press, 1961.
3. L. Breiman, "Random forests", Machine Learning, vol. 45, no. 1, pp. 5-32, 2001.
4. M. W. Kadous, "Learning comprehensible descriptions of multivariate time series", in Proceedings of the 16th International Conference on Machine Learning, ICML'99, Bled, 1999, pp. 454-463.
5. C. J. A. González and J. J. R. Diez, "Time series classification by boosting interval based literals", in Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial, 2000, pp. 2-11.
6. P. Geurts, Contributions to decision tree induction: bias/variance tradeoff and time series classification, PhD thesis, University of Liège, Belgium, 2002.
7. M. W. Kadous and C. Sammut, "Classification of multivariate time series and structured data using constructive induction", in Machine Learning, 2005, vol. 58, pp. 179-216.
8. P. Geurts and L. Wehenkel, "Segment and combine: a generic approach for supervised learning of invariant, classifiers from topologically structured data", in Machine Learning Conference of Belgium and The Netherlands, 2006, pp. 15-23.
9. P. Geurts, "Pattern extraction for time series classification", in Principles of Data Mining and Knowledge Discovery, 5th European Conference, PKDD, Freiburg, Germany, 2001, pp. 115-127.
10. C. S. Myers and L. R. Rabiner, "A. comparative study of several dynamic time-warping algorithms for connected word recognition", in The Bell System Technical Journal, 1983, vol. 607, pp. 1389-1409.
11. E. Keogh and M. Pazzani, "Dynamic time warping with higher order features", in SIAM International Conference on Data Mining, Chicago, USA, 2001.
12. I. Mierswa and K. Morik, "Automatic feature extraction, for classifying audio data", in Machine Learning, 2005, vol. 58, pp. 127-149.
13. R. T. Olszewski, Generalized feature extraction for structural pattern recognition in time-series, PhD thesis, Carnegie Mellon University, Pittsburgh, 2001.
14. B. Rice, "Techniques for developing an automatic signal classifier", in Proceedings ICSPAT, 1999.
15. C A. Ratanamahatana and E. Keogh, "Making time-series classification more accurate using learned constraints", in Proceedings of SIAM, 2004.
16. D. Berndt and J. Clifford, "Using dynamic time warping to find patterns in time series", in AAAI Workshop on Knowledge Discovery in Databases, 1994, pp. 229-248.
17. L. Breiman, "Out-of-bag estimation", Tech. Rep., University of California, Berkeley, 1996.
18. R. J. Tibshirani, "Bias, variance and prediction error for classification rules", Tech. Rep., University of Toronto, November 1997.
19. D. H. Wolpert and W. G. Macready, "An efficient method to estimate bagging's generalization error", Tech. Rep., Santa Fe Institute, 1996.
20. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees, The Wadsworth & Brooks/Cole Statistics/Probability Series. Wadsworth, 1984.
21. P. Geurts, D. Ernst, and L. Wehenkel, "Extremly randomized trees", Machine Learning, 2006.
22. J. H. Friedman, "On bias, variance, 0/1-loss and the curse of dimensionality", Data Mining and Knowledge Discovery, vol. 1, pp. 55-77, 1997.
23. L. Breiman, "Manual on setting up, using, and understanding random forests v3.1", 2002.
24. C. Chen, A. Liaw, and L. Breiman, "Using random, forest, to learn imbalanced data", Tech. Rep., University of California, Berkeley, 2004.
25. P. Domingos, "Metacost: A general method for making classifiers costsensitive", in Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, 1999, pp. 155-164.
26. C. Ling and C. Li, "Data mining for direct marketing problems and solutions", in Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, New York, 1998.
27. M. Huang, Vehicle Crash Mechanics, CRC Press, 2002.