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Hybridization of Independent Component Analysis, Rough Sets, and Multi-Objective Evolutionary Algorithms for Classificatory Decomposition of Cortical Evoked Potentials

  • Tomasz G. Smolinski
  • Grzegorz M. Boratyn
  • Mariofanna Milanova
  • Roger Buchanan
  • Astrid A. Prinz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4146)

Abstract

This article presents a continuation of our research aiming at improving the effectiveness of signal decomposition algorithms by providing them with “classification-awareness.” We investigate hybridization of multi-objective evolutionary algorithms (MOEA) and rough sets (RS) to perform the task of decomposition in the light of the underlying classification problem itself. In this part of the study, we also investigate the idea of utilizing the Independent Component Analysis (ICA) to initialize the population in the MOEA.

Keywords

Basis Function Independent Component Analysis Multiobjective Optimization Reconstruction Error Independent Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tomasz G. Smolinski
    • 1
  • Grzegorz M. Boratyn
    • 2
  • Mariofanna Milanova
    • 3
  • Roger Buchanan
    • 4
  • Astrid A. Prinz
    • 1
  1. 1.Department of BiologyEmory UniversityAtlanta
  2. 2.Kidney Disease ProgramUniversity of LouisvilleLouisville
  3. 3.Department of Computer ScienceUniversity of ArkansasLittle Rock
  4. 4.Department of BiologyArkansas State UniversityJonesboro

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