{"_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": ["user-ucsy"]}, "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", "download_preview_message": "", "file_order": 0, "filename": "9017.pdf", "filesize": [{"value": "190 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 190000.0, "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"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000004794", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-02"}, "publish_date": "2019-07-02", "publish_status": "0", "recid": "4794", "relation": {}, "relation_version_is_last": true, "title": ["Modified-MCA Based Feature Selection Model for Classification"], "weko_shared_id": -1}