{"_buckets": {"deposit": "de1bff45-2b86-423e-be6c-ca5dde4ace66"}, "_deposit": {"id": "3413", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "3413"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/3413", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Information Gain Measured Feature Selection to Reduce High Dimensional Data", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "While demand of the massive amount of datato be more effective and efficient mining strategies isincreasing significantly, practitioners andresearchers are trying to develop scalable machinelearning algorithms and strategies in turningmountains of data into nuggets. High dimension ofdata makes the memory, storage requirements andcomputational costs increased significantly.Therefore, reducing dimension can mainly improvelearning performance. Feature selection, a datapreprocessing technique, is effective and efficient toenhance data mining, data analytics and machinelearning. Most feature selection algorithms havebeen trying to eliminate irrelevant features. However,removing only irrelevant features is not enough to getthe best insight and patterns. Not only irrelevantfeatures but also redundant features can degradelearning performance. Feature selection methodswhich can eliminate both irrelevant and redundantfeatures are demanding in high dimensional dataanalytics. To solve this problem, information gainmeasured feature selection is presented in this work."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value": []}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2019-07-22"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "ICCA 2019 Proceedings Book-pages-79-84.pdf", "filesize": [{"value": "335 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 335000.0, "url": {"url": "https://meral.edu.mm/record/3413/files/ICCA 2019 Proceedings Book-pages-79-84.pdf"}, "version_id": "4222e56a-545a-4d77-a5f6-fa7ff0d07083"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Seventeenth International Conference on Computer Applications(ICCA 2019)", "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": "Win, Thee Zin"}, {"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": "2019-02-27"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/1167"}, "item_title": "Information Gain Measured Feature Selection to Reduce High Dimensional Data", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000003413", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-22"}, "publish_date": "2019-07-22", "publish_status": "0", "recid": "3413", "relation": {}, "relation_version_is_last": true, "title": ["Information Gain Measured Feature Selection to Reduce High Dimensional Data"], "weko_shared_id": -1}