Abstract
In this article we present the application of novel noise measure in ensemble method based on blind signal separation methods. In this approach we decompose the set of models’ results into basis latent components with destructive or constructive impact on the prediction. The crucial step in such model aggregation is proper identification of destructive components which can be treated as noisy factors. Presented method assesses the randomness of signals using a new measure of variability which helps to compare analyzed signal with some typical noise models. The experiments performed on electric load data using different blind separation algorithms contributed to model improvements.
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Szupiluk, R., Ząbkowski, T. (2014). Signal Randomness Measure for BSS Ensemble Predictors. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8468. Springer, Cham. https://doi.org/10.1007/978-3-319-07176-3_50
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DOI: https://doi.org/10.1007/978-3-319-07176-3_50
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