Please use this identifier to cite or link to this item: https://zone.biblio.laurentian.ca/handle/10219/2880
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dc.contributor.authorSun, Jing-
dc.date.accessioned2018-02-06T15:06:59Z-
dc.date.accessioned2018-02-06T15:07:14Z-
dc.date.available2018-02-06T15:06:59Z-
dc.date.available2018-02-06T15:07:14Z-
dc.date.issued2016-10-26-
dc.identifier.urihttps://zone.biblio.laurentian.ca/handle/10219/2880-
dc.description.abstractIn this study, we compared two feature extraction methods (PCA, PLS) and seven feature selection methods (mRMR and its variations, MaxRel, QPFS) on four different classifiers (SVM, RF, KNN, NN). We use ratio comparison validation for PCA method and 10-folds cross validation method for both the feature extraction and feature selection methods. We use Leukemia data set and Colon data set to apply the combinations and measured accuracy as well as area under ROC. The results illustrated that feature selection and extraction methods can both somehow improve the performance of classification tasks on microarray data sets. Some combinations of classifier and feature preprocessing method can greatly improve the accuracy as well as the AUC value are given in this study.en_CA
dc.language.isoenen_CA
dc.subjectMicroarray datasetsen_CA
dc.subjectFeature Extractionen_CA
dc.subjectFeature Selectionen_CA
dc.subjectPrincipal Component Analysisen_CA
dc.subjectPartial Least Squareen_CA
dc.subjectQuadratic Programming Feature Selectionen_CA
dc.subjectminimum Redundancy- Maximum Relevanten_CA
dc.subjectSupport Vector Machineen_CA
dc.subjectRandom Foresten_CA
dc.subjectk-Nearest-Neighboren_CA
dc.subjectNeural Networken_CA
dc.titleImproving classification performance of microarray analysis by feature selection and feature extraction methodsen_CA
dc.typeThesisen_CA
dc.description.degreeMaster of Science (MSc) in Computational Sciencesen_CA
dc.publisher.grantorLaurentian University of Sudburyen_CA
Appears in Collections:Master's theses
Master's Theses

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