Abstract
In this paper we apply a novel smooth component analysis algorithm as ensemble method for prediction improvement. When many prediction models are tested we can treat their results as multivariate variable with the latent components having constructive or destructive impact on prediction results. We show that elimination of those destructive components and proper mixing of those constructive can improve the final prediction results. The validity and high performance of our concept is presented on the problem of energy load prediction.
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Szupiluk, R., Wojewnik, P., Ząbkowski, T. (2007). Smooth Component Analysis as Ensemble Method for Prediction Improvement. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds) Independent Component Analysis and Signal Separation. ICA 2007. Lecture Notes in Computer Science, vol 4666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74494-8_35
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DOI: https://doi.org/10.1007/978-3-540-74494-8_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74493-1
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