Alternative quality measures for time series shapelets

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7435 LNCS, Issue , 2012, Pages 475-483

  Lines, Jason ^a   Bagnall, Anthony ^a  

^a UNIVERSITY OF EAST ANGLIA   (United Kingdom)

Author keywords

classification; shapelets; time series

Indexed keywords

CURRENT QUALITY; INFORMATION GAIN; QUALITY MEASURES; SHAPELETS; TIME SERIES CLASSIFICATIONS;

CLASSIFICATION (OF INFORMATION); STATISTICAL TESTS;

TIME SERIES;

EID: 84864966813     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-32639-4_58     Document Type: Conference Paper

References (19)

1. Bagnall, A., Davis, L., Hills, J., Lines, J.: Transformation based ensembles for time series classification. In: Proc. 12th SDM (to appear, 2012)
2. Buza, K.: Fusion methods for time-series classification. Ph.D. thesis, University of Hildesheim, Germany (2011)
3. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. JMLR 7, 1-30 (2006)
4. Deng, H., Runger, G., Tuv, E., Vladimir, M.: A time series forest for classification and feature extraction. Tech. report, Arizona State University (2011)
5. Ding, H., Trajcevski, G., Scheuermann, P.,Wang, X., Keogh, E.: Querying and mining of time series data: Experimental comparison of representations and distance measures. In: Proc. 34th VLDB (2008)
6. Hartmann, B., Link, N.: Gesture recognition with inertial sensors and optimized DTW prototypes. In: Proc. IEEE SMC (2010)
7. Hoare, C.A.R.: Quicksort. The Computer Journal 5(1), 10-16 (1962)
8. Jeong, Y., Jeong, M., Omitaomu, O.: Weighted dynamic time warping for time series classification. Pattern Recognition 44, 2231-2240 (2010)
9. Keogh, E., Kasetty, S.: On the need for time series data mining benchmarks: A survey and empirical demonstration. Data Mining and Knowledge Discovery 7(4), 349-371 (2003)
10. Kruskal, W.H.: A nonparametric test for the several sample problem. The Annals of Mathematical Statistics 23(4), 525-540 (1952)
11. Lines, J., Davis, L., Hills, J., Bagnall, A.: A shapelet transform for time series classification. In: Proc. 18th ACM SIGKDD (2012)
12. Mood, A.M.F.: Introduction to the theory of statistics. McGraw-hill (1950)
13. Mörchen, F., Mierswa, I., Ultsch, A.: Understandable models of music collections based on exhaustive feature generation with temporal statistics. In: Proc. 12th ACM SIGKDD, pp. 882-891 (2006)
14. Mueen, A., Keogh, E.J., Zhu, Q., Cash, S., Brandon Westover, M.: Exact discovery of time series motifs. In: SDM, pp. 473-484 (2009)
15. Rodriguez, J., Alonso, C.: Support vector machines of interval-based features for time series classification. Knowledge-Based Systems 18, 171-178 (2005)
16. Shannon, C.E.: A mathematical theory of communication. Bell System Technichal Journal 27, 379-423, 623-656 (1948)
17. Xing, Z., Pei, J., Yu, P., Wang, K.: Extracting interpretable features for early classification on time series. In: Proc. 11th SDM (2011)
18. Ye, L., Keogh, E.: Time series shapelets: A new primitive for data mining. In: Proc. 15th ACM SIGKDD (2009)
19. Ye, L., Keogh, E.: Time series shapelets: a novel technique that allows accurate, interpretable and fast classification. Data Min. Knowl. Discov. 22(1-2), 149-182 (2011)