Classification of multivariate time series and structured data using constructive induction

Machine Learning

Volumn 58, Issue 2-3, 2005, Pages 179-216

  Kadous, Mohammed Waleed ^a   Sammut, Claude ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

Constructive induction; Propositionalisation; Substructure; Time series

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA REDUCTION; DATA STRUCTURES; ELECTROENCEPHALOGRAPHY; LEARNING SYSTEMS; LINGUISTICS; OPTICAL CHARACTER RECOGNITION; PATTERN RECOGNITION;

CONSTRUCTIVE INDUCTION; PROPOSITIONALIZATION; SUBSTRUCTURES; TIME SERIES;

TIME SERIES ANALYSIS;

EID: 15544380168     PISSN: 08856125     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10994-005-5826-5     Document Type: Article

References (41)

1. Aurenhammer F., & Klein, R. (2000). Voronoi diagrams. In J. Sack & G. Urruita (Eds.), Handbook of Computational Geometry. Elsevier Science.
2. Bengio, Y. (1996). Neural Networks for Speech and Sequence Recognition. International Thomson Publishing Inc.
3. Blake, C. L., & Merz, C. (1998). UCI Repository of machine learning databases.
4. Box, G. E. P., & Jenkins, G. M. (1976). Time Sereis Analysis: Forecasting and Control. Holden Day.
5. Bracewell, R. N. (1965). The Fourier Transform and Its Applications. New York: McGraw-Hill.
6. Breiman, L. (1996). Bagging predictors. Machine Learning, 24, 123-140.
7. Cohen, W. W. (1995). Learning to classify English text with ILP methods. In L. D. Raedt (Ed.), Proceedings of the 5th International Workshop on Inductive Logic Programming (pp. 3-24) Department of Computer Science, Katholieke Universiteit Leuven.
8. Das, G., Lin, K.-I., Mannila, H., Renganathan, G., & Smyth, P. (1998). Rule discovery from time series. In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98). AAAI Press.
9. de Chazal, P. (1998). Automatic classification of the Frank lead electrocardiogram. Ph.D. thesis, University of New South Wales.
10. Dietterich, T. G. (2000). The divide-and-conquer manifesto. In Proceedings of the Eleventh International Conference on Algorithmic Learning Theory (pp. 13-26). Springer-Verlag.
11. Fayyad, U. M., & Irani, K. B. (1993). Multi-interval discretization of continuous-valued attributes for classification learning. In IJCAI-93: Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (pp. 1022-1027). Morgan-Kaufmann.
12. Geurts, P. (2001). Pattern extraction for time series classification. In L. de Raadt and A. Sieves (Eds.), Principles of Data Mining and Knowledge Discovery, 5th European Conference, PKDD 2001 Proceedings. Freiburg, Germany: Springer-Verlag.
13. Goodwin, G. C., Ramage, P. J., & Caines, P. E. (1980). Discrete time multivariable adaptive control. IEEE Trans. Automatic Contro, 25, 449-456.
14. Ho, Y. C., Sreenivas, R. S., & Vakili, P. (1992). Ordinal Optimization of DEDS. Discrete Event Dynamic Systems: Theory and Applications, 2(1), 61-88.
15. Ivan, B., Igor Mozetic, N. L. (1989). KARDIO: A Study in Deep and Qualitative Knowledge for Expert Systems. MIT Press.
16. Johnston, T. (1989). Auslan dictionary: A Dictionary of the Sign Language of the Australian Deaf Community. Deafness Resources Australia Ltd.
17. Kadous, M. W. (1995). GRASP: Recognition of Australian sign language using instrumented gloves. Honours Thesis.
18. Kadous, M. W. (2002). Temporal classification: Extending the classification paradigm to multivariate time series. Ph.D. thesis, School of Computer Science and Engineering, University of New South Wales.
19. Keogh, E., & Pazzani, M. (2001). Dynamic time warping with higher order features. In SIAM International Conference on Data Mining, SDM 2001. SIAM.
20. Keogh, E. J., Chakrabarti, K., Mehrotra, S., & Pazzani, M. J. (2001). Locally adaptive dimensionality reduction for indexing large time series databases. In SIGMOD Conference.
21. Lee, J. K., & Kim, H. S. (1995). Intelligent Systems for Finance and Business. Chapt. 13. John Wiley and Sons Ltd.
22. Liu, H., & Motoda, H. (Eds.). (1998). Feature Extraction, Construction and Selection: A Data Mining Perspective. Kluwer Academic Publishers.
23. Mallat, S. (1999). A Wavelet Tour of Signal Processing. Academic Press.
24. Manganaris, S. (1997). Supervised classification with temporal data. Ph.D. thesis, Computer Science Department, School of Engineering, Vanderbilt University.
25. Mannila, H., Toivonen, H., & Verkamo, A. I. (1995). Discovering frequent episodes in sequences. In Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD-95) (pp. 210-215).
26. Michalski, R. S., Mitchell, J. G., & Carbonell, T. G. (Eds.). (1983). Machine Learning: An Artificial Intelligence Approach, Chapt. A Theory and Methodology of Inductive Learning. Tioga Publishers.
27. Myers, C. S., & Rabiner, L. R. (1981). A comparative study of several dynamic time-warping algorithms for connected word recognition. The Bell System Technical Journal, 607, 1389-1409.
28. Oates, T., Schmill, M. D., & Cohen, P. R. (2000). A method for clustering the experiences of a mobile robot that accords with human judgments. In Proceedings 17th National Conference on Artificial Intelligence (pp. 846-851). AAAI Press.
29. Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers.
30. Rabiner, L. R. (1989). A Tutorial on Hidden Markov Models and selected applications in speech recognition. Proceedings of the IEEE, 77:2, 257-286.
31. Rodríguez, J. J., Alonso, C. J., & Boström, H. (2000). Learning first order logic time series classifiers. In J. Cussens, & A. Frisch (Eds.), Proceedings of ILP2000 (pp. 260-275).
32. Rosenstein, M. T., & Cohen, P. R. (1998). Concepts from time series. In AAAI '98: Fifteenth National Conference on Artificial Intelligence (pp. 739-745). AAAI Press.
33. Saito, N. (1994). Local feature extraction and its application using a library of bases. Ph.D. thesis, Yale University.
34. Schapire, R. E. (1999). A brief introduction to boosting. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence.
35. Schiller Medical (1997). The Schiller ECG Measurement and Interpretation Programs Physicians Guide.
36. Srinivarsan, A. (2000). The aleph manual. Technical report, Oxford University.
37. Statsoft (2002). Electronic Statistics Textbook ( http://www.statsoft.com/textbook/stathome.html ). Tulsa, OK: Statsoft.
38. White, A. P., & Liu, W. Z. (1994). Bias in information-based measures in decision tree induction. Machine Learning, 15, 321-329.
39. Willems, J. L., Abreu-Lima, C., Arnaud, P., Brohet, C., & Denic, B. (1990). Evaluation of ECG interpretation results obtained by computer and cardiologists. Methods of Information in Medicine, 294, 308-316.
40. Witten, I. H., & Frank, E. (1999). Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann.
41. Young, S., Kershaw, D., Odell, J., Ollason, D., Valtchev, V., & Woodland, P. (1998). The HTK Book. Microsoft Corporation.