Abstract
We present a method of constructive induction aimed at learning tasks involving multivariate time series data. Using metafeatures, the scope of attribute-value learning is expanded to domains that contain instances that have some kind of recurring substructure, such as strokes in handwriting recognition, or local maxima in time series data. These substructures are used to construct attributes. Metafeatures are applied to two real-world domains: sign language recognition and ECG classification. Using a very generic set of metafeatures we are able to generate classifiers that are either comprehensible or accurate, producing results that are comparable to hand-crafted preprocessing and comparable to human experts.
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Bengio, Y.: Neural Networks for Speech and Sequence Recognition. International Thomson Publishing Inc. (1996)
Breiman, L.: Bagging predictors. Machine Learning 24, 123–140 (1996)
Cohen, W.W.: Learning to classify English text with ILP methods. In: De Raedt, L. (ed.) Proceedings of the 5th International Workshop on Inductive Logic Programming, Department of Computer Science, pp. 3–24. Katholieke Universiteit, Leuven (1995)
de Chazal, P.: Automatic Classification of the Frank Lead Electrocardiogram. PhD thesis, University of New South Wales (1998)
Geurts, P.: Pattern extraction for time series classification. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, p. 115. Springer, Heidelberg (2001)
Ho, Y.C., Sreenivas, R.S., Vakili, P.: Ordinal optimization of DEDS. Discrete Event Dynamic Systems: Theory and Applications 2(1), 61–88 (1992)
Johnston, T.: Auslan Dictionary: a Dictionary of the Sign Language of the Australian Deaf Community. Deafness Resources Australia Ltd (1989)
Kadous, M.W.: Temporal Classification: Extending the Classification Paradigm to Multivariate Time Series. PhD thesis, School of Computer Science and Engineering, University of New South Wales, Awaiting review (2002)
Keogh, E., Pazzani, M.: Dynamic time warping with higher order features. In: SIAM International Conference on Data Mining, SDM 2001, SIAM, Philadelphia (2001)
Lee, J.K., Kim, H.S.: Intelligent Systems for Finance and Business. ch. 13. John Wiley and Sons Ltd, Chichester (1995)
Liu, H., Motoda, H. (eds.): Feature Extraction, Construction and Selection: A Data Mining Perspective. Kluwer Academic Publishers, Dordrecht (1998)
Michalski, R.S.: Machine Learning: An Artificial Intelligence Approach. In: A Theory and Methodology of Inductive Learning, Tioga Publishers (2003)
Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)
Rodríguez, J.J., Alonso, C.J., Boström, H.: Learning first order logic time series classifiers. In: Cussens, J., Frisch, A.M. (eds.) ILP 2000. LNCS (LNAI), vol. 1866, pp. 260–275. Springer, Heidelberg (2000)
Saito, N.: Local feature extraction and its application using a library of bases. PhD thesis, Yale University (December 1994)
Schapire, R.E.: A brief introduction to boosting. In: Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (1999)
Medical, S.: The Schiller ECG Measurement and Interpretation Programs Physicians Guide (1997)
Srinivarsan, A.: The Aleph manual. Technical report, Oxford University (2000)
White, A.P., Liu, W.Z.: Bias in information-based measures in decision tree induction. Machine Learning 15, 321–329 (1994)
Willems, J.L., Abreu-Lima, C., Arnaud, P., Brohet, C.R., Denic, B.: Evaluation of ECG interpretation results obtained by computer and cardiologists. Methods of Information in Medicine 29(4), 308–316 (1990)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (1999)
Young, S., Kershaw, D., Odell, J., Ollason, D., Valtchev, V., Woodland, P.: The HTK Book. Microsoft Corporation (1998)
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Kadous, M.W., Sammut, C. (2004). Constructive Induction for Classifying Time Series. In: Boulicaut, JF., Esposito, F., Giannotti, F., Pedreschi, D. (eds) Machine Learning: ECML 2004. ECML 2004. Lecture Notes in Computer Science(), vol 3201. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30115-8_20
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DOI: https://doi.org/10.1007/978-3-540-30115-8_20
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