{"created":"2020-09-01T15:21:37.414056+00:00","id":4794,"links":{},"metadata":{"_buckets":{"deposit":"d6a4516b-5617-4441-839d-222be3f8579a"},"_deposit":{"id":"4794","owners":[],"pid":{"revision_id":0,"type":"recid","value":"4794"},"status":"published"},"_oai":{"id":"oai:meral.edu.mm:recid/4794","sets":["1582963302567:1597824273898"]},"communities":["ucsy"],"item_1583103067471":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"Modified-MCA Based Feature Selection Model for Classification","subitem_1551255648112":"en"}]},"item_1583103085720":{"attribute_name":"Description","attribute_value_mlt":[{"interim":"A central problem in machine learning isidentifying a representative set of features fromwhich to construct a classification model for aparticular task. A good feature set that containshighly correlated features with the class not onlyimproves the efficiency of the classificationalgorithms but also improve the classificationaccuracy. Modified-Multiple CorrespondenceAnalysis (M-MCA or MCA with GeometricalRepresentation) explores the correlation betweendifferent features and classes to score thefeatures for feature selection. The dependencebetween a feature and a class is measured by aderived value from χ2 distance called the p-value.It is a standard measure of the reliability of arelation and is examined by p-value. The smallerthe p-value, the higher the possibility of thecorrelation between a feature and a class is true.In this paper, the conventional confidenceinterval of Multiple Correspondence Analysis(MCA) is modified to get smaller p-value and bemore reliable. To evaluate the performance ofproposed Modified-MCA, experiments arecarried out on benchmark datasets identified andprovided by WEKA and UCI repository. In theexperiments, Naïve Bayes, Decision Table andJRip are used as the classifiers. The proposedModified-MCA demonstrates promising resultsand performs better than well-known featureselection, MCA. The results show that theproposed method outperforms in terms ofclassification accuracy and reduces the size offeature subspace significantly."}]},"item_1583103108160":{"attribute_name":"Keywords","attribute_value_mlt":[{"interim":"Feature Selection"},{"interim":"Correlation"},{"interim":"Reliability"},{"interim":"P-value"},{"interim":"Confidence Interval"}]},"item_1583103120197":{"attribute_name":"Files","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2019-07-02"}],"displaytype":"preview","filename":"9017.pdf","filesize":[{"value":"190 Kb"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"url":"https://meral.edu.mm/record/4794/files/9017.pdf"},"version_id":"166ef0b5-08c4-4cba-82af-dd1db516ab1d"}]},"item_1583103131163":{"attribute_name":"Journal articles","attribute_value_mlt":[{"subitem_issue":"","subitem_journal_title":"Ninth International Conference On Computer Applications (ICCA 2011)","subitem_pages":"","subitem_volume":""}]},"item_1583103147082":{"attribute_name":"Conference papers","attribute_value_mlt":[{"subitem_acronym":"","subitem_c_date":"","subitem_conference_title":"","subitem_part":"","subitem_place":"","subitem_session":"","subitem_website":""}]},"item_1583103211336":{"attribute_name":"Books/reports/chapters","attribute_value_mlt":[{"subitem_book_title":"","subitem_isbn":"","subitem_pages":"","subitem_place":"","subitem_publisher":""}]},"item_1583103233624":{"attribute_name":"Thesis/dissertations","attribute_value_mlt":[{"subitem_awarding_university":"","subitem_supervisor(s)":[{"subitem_supervisor":""}]}]},"item_1583105942107":{"attribute_name":"Authors","attribute_value_mlt":[{"subitem_authors":[{"subitem_authors_fullname":"Khaing, Myo"},{"subitem_authors_fullname":"Kham, Nang Saing Moon"}]}]},"item_1583108359239":{"attribute_name":"Upload type","attribute_value_mlt":[{"interim":"Publication"}]},"item_1583108428133":{"attribute_name":"Publication type","attribute_value_mlt":[{"interim":"Article"}]},"item_1583159729339":{"attribute_name":"Publication date","attribute_value":"2011-05-05"},"item_1583159847033":{"attribute_name":"Identifier","attribute_value":"http://onlineresource.ucsy.edu.mm/handle/123456789/69"},"item_title":"Modified-MCA Based Feature Selection Model for Classification","item_type_id":"21","owner":"1","path":["1597824273898"],"publish_date":"2019-07-02","publish_status":"0","recid":"4794","relation_version_is_last":true,"title":["Modified-MCA Based Feature Selection Model for Classification"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2021-12-13T03:35:08.095752+00:00"}