Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/50246
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dc.contributor.authorYan, Lin
dc.date.accessioned2012-05-31T03:50:16Z
dc.date.available2012-05-31T03:50:16Z
dc.date.copyright2012en_US
dc.date.issued2012
dc.identifier.urihttp://hdl.handle.net/10356/50246
dc.description.abstractFeature selection has become the focus of much research in areas of application for which datasets with hundreds of thousands of variables are available. These areas include statistics, pattern recognition, machine learning, and knowledge discovery, gene expression array analysis, and combinatorial chemistry. With feature selection, we can improve the prediction performance of the predictors, provide faster and more cost-effective predictors, and provide a better understanding of the underlying process that generated data.en_US
dc.format.extent49 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Biometricsen_US
dc.titleInnovative feature selection methods for bioinformaticsen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorWang Lipoen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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