Ensemble based positive unlabeled learning for time series classification

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

Volumn 7238 LNCS, Issue PART 1, 2012, Pages 243-257

  Nguyen, Minh Nhut ^a   Li, Xiao Li ^a   Ng, See Kiong ^a  

^a INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

Author keywords

Ensemble based system; positive and unlabeled learning; time series classification

Indexed keywords

CLASS BOUNDARY; DETECTION PHASE; DIFFERENT DOMAINS; ENSEMBLE BASED SYSTEM; NEGATIVE EXAMPLES; POSITIVE AND UNLABELED LEARNING; POSITIVE EXAMPLES; REAL-WORLD APPLICATION; STATE OF THE ART; TIME SERIES CLASSIFICATIONS; TIME-SERIES DATA; TRAINING EXAMPLE;

LEARNING ALGORITHMS;

DATABASE SYSTEMS;

EID: 84860651757     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-29038-1_19     Document Type: Conference Paper

References (22)

1. Olszewski, R.T.: Generalized Feature Extraction for Structural Pattern Recognition in Time-Series Data, PhD thesis, Carnegie Mellon University, Pittsburgh, PA (2001)
2. Rath, T.M., Manmatha, R.: Word image matching using dynamic time warping. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. II-521-II-527 (2003)
3. Xi, X., Keogh, E., Shelton, C., Wei, L., Ratanamahatana, C.A.: Fast time series classification using numerosity reduction. In: Proceedings of the 23rd International Conference on Machine Learning. ACM, Pittsburgh (2006)
4. Chapelle, O., Scholkopf, B., Zien, A.: Semi-Supervised Learning. MIT Press (2006) (in Press)
5. Li, M., Zhou, Z.-H.: SETRED: Self-Training with Editing. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 611-621. Springer, Heidelberg (2005)
6. Zhu, X.: Semi-supervised learning literature survey, Technical report, no.1530, Computer Sciences, University of Wisconsin-Madison (2008)
7. Liu, T., Du, X., Xu, Y., Li, M.-H., Wang, X.: Partially Supervised Text Classification with Multi-Level Examples. In: AAAI (2011)
8. Gabriel Pui Cheong, F., Yu, J.X., Hongjun, L., Yu, P.S.: Text classification without negative examples revisit. IEEE Transactions on Knowledge and Data Engineering 18, 6-20 (2006)
9. Li, X., Liu, B.: Learning to classify texts using positive and unlabeled data. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence. Morgan Kaufmann Publishers Inc., Acapulco (2003)
10. Li, X., Liu, B., Ng, S.-K.: Learning to Identify Unexpected Instances in the Test Set. In: Proceedings of Twentieth International Joint Conference on Artificial Intelligence, India (IJCAI 2007), pp. 2802-2807 (2007)
11. Li, X., Yu, P., Liu, B., Ng, S.-K.: Positive Unlabeled Learning for Data Stream Classification. In: SDM, pp. 257-268 (2009)
12. Liu, B., Lee, W.S., Yu, P.S., Li, X.: Partially Supervised Classification of Text Documents. In: ICML (2002)
13. Elkan, C., Noto, K.: Learning Classifiers from Only Positive and Unlabeled Data. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2008)
14. Wei, L., Keogh, E.: Semi-supervised time series classification. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, Philadelphia (2006)
15. Ratanamahatana, C., Wanichsan, D.: Stopping Criterion Selection for Efficient Semi-supervised Time Series Classification. In: Lee, R. (ed.) Soft. Eng., Arti. Intel., Net. & Para./Distri. Comp. SCI, vol. 149, pp. 1-14. Springer, Heidelberg (2008)
16. Nguyen, M.N., Li, X., Ng, S.-K.: Positive Unlabeled Learning for Time Series Classification. In: Proceedings of International Joint Conference on Artificial Intelligence, IJCAI (2011)
17. Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm: analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 881-892 (2002)
18. Keogh, E., Kasetty, S.: On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. Data Mining and Knowledge Discovery 7, 349-371 (2003)
19. Yoon, H., Yang, K., Shahabi, C.: Feature subset selection and feature ranking for multivariate time series. IEEE Transactions on Knowledge and Data Engineering 17, 1186-1198 (2005)
20. Wilson, D.L.: Asymptotic Properties of Nearest Neighbor Rules Using Edited Data. IEEE Transactions on Systems, Man and Cybernetics 2, 408-421 (1972)
21. Wei, L.: Self Training dataset (2007), http://alumni.cs.ucr.edu/~wli/ selfTraining/
22. Keogh, E.: The UCR Time Series Classification/Clustering Homepage (2008), http://www.cs.ucr.edu/~eamonn/time-series-data/