Hybridization of independent component analysis, rough sets, and multi-objective evolutionary algorithms for classificatory decomposition of cortical evoked potentials

Authors:
Tomasz G. Smolinski;Grzegorz M. Boratyn;Mariofanna Milanova;Roger Buchanan;Astrid A. Prinz
Affiliations:
Department of Biology, Emory University, Atlanta, GA;Kidney Disease Program, University of Louisville, Louisville, KY;Department of Computer Science, University of Arkansas, Little Rock, AR;Department of Biology, Arkansas State University, Jonesboro, AR;Department of Biology, Emory University, Atlanta, GA
Venue:
PRIB'06 Proceedings of the 2006 international conference on Pattern Recognition in Bioinformatics
Year:
2006

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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.