Abstract
Many feature extraction algorithms have been proposed for time series classification. However, most of the proposed algorithms in time series data mining community belong to the unsupervised approach, without considering the class separability capability of features that is important for classification. In this paper we propose a supervised feature extraction approach by selecting discriminating wavelet coefficients to improve the time series classification accuracy. After wavelet transformation, few wavelet coefficients with higher class separability capability are selected as features. We apply three feature evaluation criteria, i.e., Fisher’s discriminant ratio, divergence, and Bhattacharyya distance. Experiments performed on several benchmark time series datasets demonstrate the effectiveness of the proposed approach.
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References
Randy, S.J., Jain, A.K.: Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners. IEEE Trans. on Pattern Analysis and Machine Intelligence 13, 252–264 (1991)
Mallat, S.: A Wavelet Tour of Signal Processing, 2nd edn. Academic Press, San Diego (1999)
Chan, K.P., Fu, A.W., Clement, T.Y.: Harr Wavelets for Efficient Similarity Search of Time-Series: with and without Time Warping. IEEE Trans. on Knowledge and Data Engineering 15, 686–705 (2003)
M\(\ddot{o}\)rchen, F.: Time Series Feature Extraction for Data Mining Using DWT and DFT. Technical Report No. 33, Dept. of Machmatics and Computer Science, University of Marburg, Germany (2003)
Zhang, H., Ho, T.B., Lin, M.S.: A Non-Parametric Wavelet Feature Extractor for Time Series Classification. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 595–603. Springer, Heidelberg (2004)
Zhang, H., Ho, T.B., Huang, W.: Blind Feature Extraction for Time-Series Classification Using Haar Wavelet Transform. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3497, pp. 605–610. Springer, Heidelberg (2005)
Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn., San Diego, CA (1990)
Specht, D.F.: Probabilistic Neural Networks. Neural Networks 11, 109–118 (1990)
Keogh, E., Folias, T.: The UCR Time Series Data Mining Archive (2002), http://www.cs.ucr.edu/~eamonn/TSDMA/index.html
Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: Components of A New Research Resource for Complex Physiologic Signals. Circulation 101, e215–e220 (2000), http://www.physionet.org/physiobank/database
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Zhang, H., Ho, T.B., Lin, MS., Liang, X. (2006). Feature Extraction for Time Series Classification Using Discriminating Wavelet Coefficients. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_207
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DOI: https://doi.org/10.1007/11759966_207
Publisher Name: Springer, Berlin, Heidelberg
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