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.
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Keywords
- Basis Function
- Independent Component Analysis
- Multiobjective Optimization
- Reconstruction Error
- Independent Component Analysis
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Smolinski, T.G., Boratyn, G.M., Milanova, M., Buchanan, R., Prinz, A.A. (2006). Hybridization of Independent Component Analysis, Rough Sets, and Multi-Objective Evolutionary Algorithms for Classificatory Decomposition of Cortical Evoked Potentials. In: Rajapakse, J.C., Wong, L., Acharya, R. (eds) Pattern Recognition in Bioinformatics. PRIB 2006. Lecture Notes in Computer Science(), vol 4146. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11818564_19
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DOI: https://doi.org/10.1007/11818564_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37446-6
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