Extracting interpretable features for early classification on time series

Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011

Volumn , Issue , 2011, Pages 247-258

  Xing, Zhengzheng ^a   Pei, Jian ^a   Yu, Philip S ^b   Wang, Ke ^a  

^a SIMON FRASER UNIVERSITY   (Canada)

^b UNIVERSITY OF ILLINOIS AT CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCIDENT PREVENTION; DATA MINING; INDUSTRIAL MANAGEMENT; MEDICAL INFORMATICS; TIME SERIES;

DOMAIN EXPERTS; INTERPRETABILITY; MEDICAL DOCTORS; PRODUCTION MANAGEMENT; REAL DATA SETS; SAFETY AND SECURITIES; TARGET CLASS; TIME-SERIES DATA;

CLASSIFICATION (OF INFORMATION);

EID: 84873606971     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972818.22     Document Type: Conference Paper

References (16)

1. A. O. Allen. Probability, Statistics, and Queuing Theory with Computer Science Applications. Acadimic Press, Inc., San Diago, CA, 1990.
2. L. Bernaille, R. Teixeira, I. Akodkenou, A. Soule, and K. Salamatian. Traffic classification on the fly. Computer Communication Review, 36(2):23-26, 2006.
3. A. Bregón, M. A. Simón, J. J. Rodríguez, C. J. Alonso, B. P. Junquera, and I. Moro. Early fault classification in dynamic systems using case-based reasoning. In CAEPIA, pages 211-220, 2005.
4. J. J. R. Diez, C. A. González, and H. Boström. Boosting interval based literals: variable length and early classification. Intell. Data Anal., 5(3):245-262, 2001.
5. M. P. Griffin and J. R. Moorman. Toward the early diagnosis of neonatal sepsis and sepsis-like illness using novel heart rate analysis. PEDIATRICS, 107(1):97-104, 2001.
6. E. Keogh, X. Xi, L. Wei, and C. A. Ratanamahatana. The UCR time series classification and clustering homepage: http://www.cs.ucr.edu/~eamonn/time- series-data/, 2006.
7. B. Liu, W. Hsu, and Y. Ma. Integrating classification and association rule mining. In KDD '98.
8. M. D. Marzio and C. C. Taylor. Kernel density classification and boosting: an l2 analysis. Statistics and Computing, 15(2):113-123, 2005.
9. T. M. Mitchell. Machine Learning. McGraw-Hill, New York, 1997.
10. C. A. Ratanamahatana and E. J. Keogh. Making time-series classification more accurate using learned constraints. In SDM '04.
11. D. W. Scott. Multivariate Density Estimation: Theory, Practice, and Visualization. Wiley, 1992.
12. A.S. Weigend and N.A. Gershenfeld, eds. Time Series Prediction: Forecasting the Future and Understanding the Past. Addison-Wesley.
13. Z. Xing, J. Pei, G. Dong, and P. S. Yu. Mining sequence classifiers for early prediction. In SDM'08: Proceedings of the 2008 SIAM international conference on data mining, pages 644-655, 2008.
14. Z. Xing, J. Pei, and E. Keogh. A brief survey on sequence classification. ACM SIGKDD Explorations, Volume 12, Issue 1, pages 40-48, June 2010, ACM Press.
15. Z. Xing, J. Pei, and P. S. Yu. Early classification on time series: A nearest neighbor approach. In IJCAI'09.
16. L. Ye and E. Keogh. Time series shapelets: A new primitive for data mining. In KDD '09.