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Extended AMUSE Algorithm and Novel Randomness Approach for BSS Model Aggregation with Methodology Remarks

  • Ryszard Szupiluk
  • Tomasz Ząbkowski
  • Krzysztof Gajowniczek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9120)

Abstract

In this paper we propose application of extended AMUSE blind signal separation method to improve a model prediction. In our approach we assume, that results generated by any regression model usually include both constructive and destructive components. In case of a few models, some of the components can be common to all of them. Our aim is to find the basis elements via AMUSE algorithm and distinguish the components with the constructive influence on the modelling quality from the destructive ones. We extend the standard AMUSE algorithm for cases with strong noises. The crucial question is to determine number of delays used in separation process and define criterion for destructive components identification. We propose novel method of randomness analysis to solve above problems. Due to complexity of the whole BSS aggregation method we include some methodological remarks as the framework for proposed approach.

Keywords

Blind signal separation Models aggregation AMUSE algorithm 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ryszard Szupiluk
    • 1
    • 2
  • Tomasz Ząbkowski
    • 3
  • Krzysztof Gajowniczek
    • 3
    • 4
  1. 1.T-Mobile Polska S.AWarsawPoland
  2. 2.Warsaw School of EconomicsWarsawPoland
  3. 3.Warsaw University of Life SciencesWarsawPoland
  4. 4.Systems Research InstitutePolish Acad. of SciencesWarsawPoland

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