{"_buckets": {"deposit": "90cc725b-9dc1-424b-b99c-ac254892a26e"}, "_deposit": {"id": "4850", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "4850"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/4850", "sets": ["1597824273898", "user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Modified-MCA: An Effective Feature Selection", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Feature selection is often an essential data processing step prior to applying a learning algorithm. The removal of irrelevant and redundant information improves the performance of machine learning algorithms. In this paper, Modified-Multiple Correspondence Analysis (M-MCA) is proposed. It explores the correlation between different features and classes to score the features for feature selection. To evaluate the performance of proposed Modified-MCA, experiments are carried out on ten benchmark datasets. In the experiments, AdaBoost, Decision Table, JRip, Naïve Bayes, and Sequential Minimal Optimization (SMO) are used as the classifiers. The proposed Modified-MCA demonstrates the promising classification results and performs better than other well-known feature selection methods; Information Gain and Relief."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Correlation"}, {"interim": "Reliability"}, {"interim": "Confidence Interval"}, {"interim": "P-value"}, {"interim": "feature selection"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value": []}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Eleventh International Conference On Computer Applications (ICCA 2013)", "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": "2013-02-26"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/748"}, "item_title": "Modified-MCA: An Effective Feature Selection", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000004850", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-11"}, "publish_date": "2019-07-11", "publish_status": "0", "recid": "4850", "relation": {}, "relation_version_is_last": true, "title": ["Modified-MCA: An Effective Feature Selection"], "weko_shared_id": -1}