Show simple item record Nikouline, Alexandre en_US 2007-05-17T12:39:15Z 2007-05-17T12:39:15Z 1998-03-01T00:00:00Z en_US
dc.description.abstract We introduce a global feature extraction method specifically designed to preprocess magnetic resonance spectra of biomedical origin. Such preprocessing is essential for the accurate and reliable classification of diseases or disease stages manifest in the spectra. The new method is Genetic Algorithm-guided. It is compared with our enhanced version of the Forward Selection algorithm ("Dynamic Programming"). Both seek and select optimal spectral subregions. These subregions necessarily retain spectral information, thus aiding the eventual identification of the biochemistry of disease presence and progression. Both methods proved to be very useful for large datasets. The danger of overfitting related to the small number of samples in the datasets was demonstrated for both the artificial and real-life data. A bilinear regression model was used to quantitate the consequences of overfitting. Taking this in account, optimal parameters for the GA guided algorithm were recommended. en_US
dc.format.extent 4246494 bytes
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dc.language en en_US
dc.language.iso en_US
dc.title New preprocessing methods for better classification of MR and IR spectra en_US Electrical and Computer Engineering en_US Doctor of Philosophy (Ph.D.) en_US

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