New preprocessing methods for better classification of MR and IR spectra

New preprocessing methods for better classification of MR and IR spectra

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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...

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Main Author: Nikouline, Alexandre
Format: Others
Language:en
en_US
Published: 2007
Online Access:http://hdl.handle.net/1993/1521
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spelling ndltd-LACETR-oai-collectionscanada.gc.ca-MWU.anitoba.ca-dspace#1993-15212013-01-11T13:30:41ZNikouline, Alexandre2007-05-17T12:39:15Z2007-05-17T12:39:15Z1998-03-01T00:00:00Zhttp://hdl.handle.net/1993/1521We 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.4246494 bytes184 bytesapplication/pdftext/plainenen_USNew preprocessing methods for better classification of MR and IR spectraElectrical and Computer EngineeringPh.D.
collection NDLTD
language en
en_US
format Others
sources NDLTD
description 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.
author Nikouline, Alexandre
spellingShingle Nikouline, Alexandre
New preprocessing methods for better classification of MR and IR spectra
author_facet Nikouline, Alexandre
author_sort Nikouline, Alexandre
title New preprocessing methods for better classification of MR and IR spectra
title_short New preprocessing methods for better classification of MR and IR spectra
title_full New preprocessing methods for better classification of MR and IR spectra
title_fullStr New preprocessing methods for better classification of MR and IR spectra
title_full_unstemmed New preprocessing methods for better classification of MR and IR spectra
title_sort new preprocessing methods for better classification of mr and ir spectra
publishDate 2007
url http://hdl.handle.net/1993/1521
work_keys_str_mv AT nikoulinealexandre newpreprocessingmethodsforbetterclassificationofmrandirspectra
_version_ 1716574745974013952

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