The noise identification method based on divergence analysis in ensemble methods context

Abstract

In this paper we propose a divergence based method for noise detection in ensemble method context where the prediction results from different models are treated as a multidimensional variable that contains constructive and destructive latent components. The crucial stage is the proper destructive and constructive components classification. We propose to calculate the noisiness of the particular latent component as the divergence from chosen reference noise. It allows us to identify the wide range of noises besides the typical signals with close analytical form such as Gaussian or uniform. The real data experiment with load energy prediction confirms presented methodology. © 2011 Springer-Verlag.