{"id":{"repo_id":"laurentian","oai_identifier":"oai:laurentian.scholaris.ca:10219/2880"},"canonical_url":"https://search.dev.ndltd.org/etd/laurentian/oai:laurentian.scholaris.ca:10219/2880","repository":{"repo_id":"laurentian","name":"Laurentian University","base_url":"https://laurentian.scholaris.ca/server/oai/request"},"display":{"title":"Improving classification performance of microarray analysis by feature selection and feature extraction methods","abstract":"In 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.","abstract_html":"In 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.","abstract_has_math":false,"creators":["Sun, Jing"],"institution":"Laurentian University of Sudbury","degree_name":"Master of Science (MSc) in Computational Sciences","degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-10-26","date_published":"2016-10-26","updated_at":"2026-08-21T16:45:57Z","subjects":["Microarray datasets","Feature Extraction","Feature Selection","Principal Component Analysis","Partial Least Square","Quadratic Programming Feature Selection","minimum Redundancy- Maximum Relevant","Support Vector Machine","Random Forest","k-Nearest-Neighbor","Neural Network"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://laurentian.scholaris.ca/handle/10219/2880","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"source_record":{"url":"https://laurentian.scholaris.ca/server/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Alaurentian.scholaris.ca%3A10219%2F2880","prefix":"dim"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Sun, Jing"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-02-06T15:06:59Z","2018-02-06T15:07:14Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-02-06T15:06:59Z","2018-02-06T15:07:14Z"]},{"key":"dc:date.issued","label":"Date","values":["2016-10-26"]},{"key":"dc:publisher","label":"Institution","values":["Laurentian University of Sudbury"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MSc) in Computational Sciences"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Laurentian University of Sudbury"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Microarray datasets","Feature Extraction","Feature Selection","Principal Component Analysis","Partial Least Square","Quadratic Programming Feature Selection","minimum Redundancy- Maximum Relevant","Support Vector Machine","Random Forest","k-Nearest-Neighbor","Neural Network"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://laurentian.scholaris.ca/handle/10219/2880"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In 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."]},{"key":"dc:title","label":"Title","values":["Improving classification performance of microarray analysis by feature selection and feature extraction methods"]}]}],"canonical_facts":{"dc:creator":["Sun, Jing"],"dc:date.accessioned":["2018-02-06T15:06:59Z","2018-02-06T15:07:14Z"],"dc:date.available":["2018-02-06T15:06:59Z","2018-02-06T15:07:14Z"],"dc:date.issued":["2016-10-26"],"dc:description.abstract":["In 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."],"dc:identifier.uri":["https://laurentian.scholaris.ca/handle/10219/2880"],"dc:language.iso":["en"],"dc:publisher":["Laurentian University of Sudbury"],"dc:subject":["Microarray datasets","Feature Extraction","Feature Selection","Principal Component Analysis","Partial Least Square","Quadratic Programming Feature Selection","minimum Redundancy- Maximum Relevant","Support Vector Machine","Random Forest","k-Nearest-Neighbor","Neural Network"],"dc:title":["Improving classification performance of microarray analysis by feature selection and feature extraction methods"],"dc:type":["Thesis"],"thesis:degree_name":["Master of Science (MSc) in Computational Sciences"],"thesis:institution_name":["Laurentian University of Sudbury"]},"updated_at":"2026-08-21T16:45:57Z"}