On the stopping criteria for k-Nearest Neighbor in positive unlabeled time series classification problems

Information Sciences

Volumn 328, Issue , 2016, Pages 42-59

  González, Mabel ^a   Bergmeir, Christoph ^b   Triguero, Isaac ^c,d   Rodríguez, Yanet ^a   Benítez, José M ^e  

^a CENTRAL UNIVERSITY OF LAS VILLAS   (Cuba)

^b MONASH UNIVERSITY   (Australia)

^c GHENT UNIVERSITY   (Belgium)

^d DEPARTMENT OF PLANT SYSTEMS BIOLOGY   (Belgium)

^e UNIVERSITY OF GRANADA   (Spain)

Author keywords

k Nearest Neighbor; Positive unlabeled learning; Self training; Time series classification; Transductive learning

Indexed keywords

MOTION COMPENSATION; NEAREST NEIGHBOR SEARCH; TIME SERIES;

K-NEAREST NEIGHBORS; POSITIVE UNLABELED LEARNING; SELF TRAINING; TIME SERIES CLASSIFICATIONS; TRANSDUCTIVE LEARNING;

CLASSIFICATION (OF INFORMATION);

EID: 84945536443     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2015.07.061     Document Type: Article

References (47)

1. D.W. Aha, D. Kibler, and M.K. Albert Instance-based learning algorithms Mach. Learn. 6 1 1991 37 66
2. G. Batista, Y. Hao, E. Keogh, and A. Mafra-Neto Towards automatic classification on flying insects using inexpensive sensors Proceedings of the 10th International Conference on Machine Learning and Applications (ICMLA) vol. 1 2011 IEEE 364 369
3. N. Begum, B. Hu, T. Rakthanmanon, and E. Keogh A minimum description length technique for semi-supervised time series classification Integration of Reusable Systems. Advances in Intelligent Systems and Computing vol. 263 2014 Springer International Publishing 171 192
4. B. Bergmann, and G. Hommel Improvements of general multiple test procedures for redundant systems of hypotheses Multiple Hypotheses Testing. Medizinische Informatik und Statistik vol. 70 1988 Springer Berlin Heidelberg 100 115
5. A. Blum, and T. Mitchell Combining labeled and unlabeled data with co-training Proceedings of the 11th Annual Conference on Computational Learning Theory 1998 ACM 92 100
6. G. Bruno, and P. Garza Temporal pattern mining for medical applications Intell. Syst. Ref. Libr. 25 2012 9 18
7. B. Calvo, P. Larrañaga, and J.A. Lozano Learning Bayesian classifiers from positive and unlabeled examples Pattern Recognit. Lett. 28 16 2007 2375 2384
8. B. Calvo, N. López-Bigas, S.J. Furney, P. Larrañaga, and J.A. Lozano A partially supervised classification approach to dominant and recessive human disease gene prediction Comput. Methods Progr. Biomed. 85 3 2007 229 237
9. O. Chapelle, B. Schölkopf, and A. Zien Semi-Supervised Learning 2 2006 MIT Press Cambridge
10. Y. Chen, B. Hu, E. Keogh, and G.E. Batista DTW-D: time series semi-supervised learning from a single example Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '13 2013 ACM New York, NY, USA 383 391 10.1145/2487575.2487633
11. F. Denis, R. Gilleron, A. Laurent, and M. Tommasi Text classification and co-training from positive and unlabeled examples Proceedings of the ICML 2003 Workshop: The Continuum from Labeled to Unlabeled Data 2003 80 87
12. F. Denis, R. Gilleron, and M. Tommasi Text classification from positive and unlabeled examples Proceedings of the 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU '02 2002 1927 1934
13. R.D. Edwards, J. Magee, and W.H.C. Bassetti Technical Analysis of Stock Trends 10th ed. 2012 CRC Press Taylor & Francis Group
14. S. Flesca, G. Manco, E. Masciari, L. Pontieri, and A. Pugliese Exploiting structural similarity for effective web information extraction Data Knowl. Eng. 60 1 2007 222 234
15. M. Friedman The use of ranks to avoid the assumption of normality implicit in the analysis of variance J. Am. Stat. Assoc. 32 200 1937 675 701
16. T. Fu A review on time series data mining Eng. Appl. Artif. Intell. 24 1 2011 164 181
17. S. García, A. Fernández, J. Luengo, and F. Herrera Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power Inf. Sci. 180 10 2010 2044 2064
18. S. García, and F. Herrera An extension on "statistical comparisons of classifiers over multiple data sets" for all pairwise comparisons J. Mach. Learn. Res. 9 2008 2695 2698
19. A.L. Goldberger, L.A. Amaral, L. Glass, J.M. Hausdorff, P.C. Ivanov, R.G. Mark, J.E. Mietus, G.B. Moody, C. Peng, and H.E. Stanley Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals Circulation 101 23 2000 e215 e220
20. P.D. Grünwald, I.J. Myung, and M.A. Pitt Advances in Minimum Description Length: Theory and Applications 2005 MIT Press
21. Y. Hochberg, and D. Rom Extensions of multiple testing procedures based on Simes' test J. Stat. Plan. Inference 48 2 1995 141 152
22. J. Hodges, and E.L. Lehmann Rank methods for combination of independent experiments in analysis of variance Ann. Math. Stat. 33 2 1962 482 497
23. E. Keogh, Q. Zhu, B. Hu, Y. Hao, X. Xi, L. Wei, C.A. Ratanamahatana, The UCR time series classification/clustering homepage, 2011.
24. C.D. Kirkpatrick, and J.R. Dahlquist Technical Analysis: The Complete Resource for Financial Market Technicians 2010 FT Press
25. V. Kurbalija, M. Radovanović, Z. Geler, and M. Ivanović The influence of global constraints on similarity measures for time-series databases Knowl. Based Syst. 56 2014 49 67
26. H. Li On-line and dynamic time warping for time series data mining Int. J. Mach. Learn. Cybern. 6 1 2015 145 153 10.1007/s13042-014-0254-0
27. X. Li, B. Liu, and S. Ng Learning to identify unexpected instances in the test set Proceedings of the International Joint Conferences on Artificial Intelligence vol. 7 2007 2802 2807
28. X. Li, B. Liu, and S. Ng Negative training data can be harmful to text classification Proceedings of the Conference on Empirical Methods in Natural Language Processing 2010 218 228
29. B. Liu, Y. Dai, X. Li, W.S. Lee, and P.S. Yu Building text classifiers using positive and unlabeled examples Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM) 2003 179 186
30. M.N. Nguyen, X. Li, and S. Ng Positive unlabeled learning for time series classification Proceedings of the 22nd International Joint Conference on Artificial Intelligence-Volume Two 2011 AAAI Press 1421 1426
31. M.N. Nguyen, X. Li, and S. Ng Ensemble based positive unlabeled learning for time series classification Proceedings of the Database Systems for Advanced Applications 2012 Springer 243 257
32. P. Ordonez, T. Oates, M. Lombardi, G. Hernandez, K.W. Holmes, J. Fackler, and C.U. Lehmann Visualization of multivariate time-series data in a neonatal ICU IBM J. Res. Dev. 56 5 2012 7:1 7:12
33. C.A. Ratanamahatana, and D. Wanichsan Stopping criterion selection for efficient semi-supervised time series classification R. Lee, Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing Studies in Computational Intelligence vol. 149 2008 Springer Berlin Heidelberg 1 14
34. H. Sakoe, and S. Chiba Dynamic programming algorithm optimization for spoken word recognition IEEE Trans. Acoust. Speech Signal Process. 26 1 1978 43 49
35. J. Serrà, and J.L. Arcos An empirical evaluation of similarity measures for time series classification Knowl. Based Syst. 67 0 2014 305 314 10.1016/j.knosys.2014.04.035
36. D.M. Tax, and R.P. Duin Uniform object generation for optimizing one-class classifiers J. Mach. Learn. Res. 2 2002 155 173
37. V. Thanh Vinh, and D. Tuan Anh Some novel improvements for MDL-based semi-supervised classification of time series Computational Collective Intelligence. Technologies and Applications. Lecture Notes in Computer Science 8733 2014 Springer International Publishing 483 493
38. I. Triguero, S. García, and F. Herrera Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study Knowl. Inf. Syst. 2013 1 40 10.1007/s10115-013-0706-y
39. X. Wang, A. Mueen, H. Ding, G. Trajcevski, P. Scheuermann, and E. Keogh Experimental comparison of representation methods and distance measures for time series data Data Min. Knowl. Discov. 26 2 2013 275 309
40. X. Wang, H. Xing, Y. Li, Q. Hua, C. Dong, and W. Pedrycz A study on relationship between generalization abilities and fuzziness of base classifiers in ensemble learning IEEE Trans. Fuzzy Syst. PP 99 2014 1
41. L. Wei, Datasets used for experimental evaluation in the paper: Semi-supervised time series classification, 2006.
42. L. Wei, and E. Keogh Semi-supervised time series classification Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2006 748 753
43. I.H. Witten, and E. Frank Data Mining: Practical Machine Learning Tools and Techniques 2005 Morgan Kaufmann
44. D. Yarowsky Unsupervised word sense disambiguation rivaling supervised methods Proceedings of the 33rd Annual Meeting on Association for Computational Linguistics 1995 Association for Computational Linguistics 189 196
45. S. Yu, and C. Li PE-PUC: a graph based PU-learning approach for text classification 4571 LNAI 2007 574 584
46. B. Zhang, and W. Zuo Reliable negative extracting based on kNN for learning from positive and unlabeled examples J. Comput. 4 1 2009 94 101
47. X. Zhu, and A.B. Goldberg Introduction to Semi-Supervised Learning 2009 Morgan & Claypool Publishers