Abstract
A problem of learning from a database where each sample consists of several time series and a single response is considered. We are interested in maximum data reduction that preserves predictive power of the original time series, and at the same time allows reasonable reconstruction quality of the original signals. Each signal is decomposed into a set of wavelet features that are coded according to their importance consisting of two terms. The first depends on the influence of the feature on the expected signal reconstruction error, and the second is determined by feature importance for the response prediction. The latter is calculated by building series of boosted decision tree ensembles. We demonstrate that such combination maintains small signal distortion rates, and ensures no increase in the prediction error in contrast to the unsupervised compression with the same reduction ratio.
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Eruhimov, V., Martyanov, V., Raulefs, P., Tuv, E. (2006). Combining Unsupervised and Supervised Approaches to Feature Selection for Multivariate Signal Compression. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_58
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DOI: https://doi.org/10.1007/11875581_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
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