{"created":"2024-06-03T01:24:51.249307+00:00","id":2000135,"links":{},"metadata":{"_buckets":{"deposit":"7661371f-775d-413b-8e08-bbde21dd3898"},"_deposit":{"created_by":17,"id":"2000135","owners":[17],"pid":{"revision_id":0,"type":"depid","value":"2000135"},"status":"published"},"_oai":{"id":"oai:shukutoku.repo.nii.ac.jp:02000135","sets":["171:1717050505131"]},"author_link":[],"control_number":"2000135","item_2_biblio_info_12":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2024-02-25","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"112","bibliographicPageStart":"93","bibliographicVolumeNumber":"68","bibliographic_titles":[{"bibliographic_title":"淑徳大学短期大学部研究紀要","bibliographic_titleLang":"ja"},{"bibliographic_title":"Shukutoku University Junior College bulletin","bibliographic_titleLang":"en"}]}]},"item_2_description_10":{"attribute_name":"抄録(日)","attribute_value_mlt":[{"subitem_description":"機械学習を用いた離職予測に関する先行研究をサーベイし、離職者を予測するためのアルゴリズムとその予測精度及び離職に影響を与える特徴量を明らかにした。アルゴリズム単体で集計すると、最も良く使われているアルゴリズムはRF（11/15：15文献中11文献で使用）であり、続いてLR、NB（9/15）、DT（8/15）、SVM、KNN（7/15）であった。アルゴリズムの要素や特徴で集計すると、最も良く使われていたのはアンサンブル学習であった（13/15）。データセットや前処理等が異なるため、単純に比較することはできないが、指標だけで比較すれば、最も精度が高かったアルゴリズムはRFであった（ACC=.9940, AUC=1.000）。\n離職に影響を与える代表的な特徴量は給与・昇進・昇給、個人属性、内部要因（企業内要因）、有給取得、出張、福利厚生（寮）、残業であった。研究において使用されているデータセットのソースは極めて少数（１～２個）であり、一般化が不十分という問題点がある。特徴量については離職の大きな原因とされている職場の人間関係や社風・組織風土、ライフイベント、健康状態などがほとんど使用されていないことが問題点としてあげられる。","subitem_description_language":"ja","subitem_description_type":"Other"}]},"item_2_description_15":{"attribute_name":"表示順","attribute_value_mlt":[{"subitem_description":"10","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"記事種別(日)","attribute_value_mlt":[{"subitem_description":"論文","subitem_description_language":"ja","subitem_description_type":"Other"}]},"item_2_source_id_1":{"attribute_name":"雑誌書誌ID","attribute_value_mlt":[{"subitem_source_identifier":"AA12752621","subitem_source_identifier_type":"NCID"}]},"item_2_source_id_19":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"21887438","subitem_source_identifier_type":"ISSN"}]},"item_2_text_6":{"attribute_name":"著者所属(日)","attribute_value_mlt":[{"subitem_text_language":"ja","subitem_text_value":"淑徳大学短期大学部健康福祉学科"}]},"item_2_title_3":{"attribute_name":"論文名よみ","attribute_value_mlt":[{"subitem_title":"キカイ ガクシュウ オ モチイタ リショク ヨソク ニカンスル ブンケン レビュー","subitem_title_language":"ja"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"三田寺, 裕治","creatorNameLang":"ja"},{"creatorName":"ミタデラ, ユウジ","creatorNameLang":"ja-Kana"}]},{"creatorNames":[{"creatorName":"MITADERA, Yuji","creatorNameLang":"en"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","displaytype":"detail","filename":"tandaikenkyukiyo_068_093_112.pdf","format":"application/pdf","mimetype":"application/pdf","url":{"url":"https://shukutoku.repo.nii.ac.jp/record/2000135/files/tandaikenkyukiyo_068_093_112.pdf"},"version_id":"498b4e35-5034-443d-9433-e38dc6de29f7"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"機械学習","subitem_subject_language":"ja","subitem_subject_scheme":"Other"},{"subitem_subject":"AI","subitem_subject_language":"ja","subitem_subject_scheme":"Other"},{"subitem_subject":"離職予測","subitem_subject_language":"ja","subitem_subject_scheme":"Other"},{"subitem_subject":"リテンションマネジメント","subitem_subject_language":"ja","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"departmental bulletin paper","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"機械学習を用いた離職予測に関する文献レビュー","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"機械学習を用いた離職予測に関する文献レビュー","subitem_title_language":"ja"},{"subitem_title":"Literature Review on Turnover Prediction Using Machine Learning","subitem_title_language":"en"}]},"item_type_id":"2","owner":"17","path":["1717050505131"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2024-06-03"},"publish_date":"2024-06-03","publish_status":"0","recid":"2000135","relation_version_is_last":true,"title":["機械学習を用いた離職予測に関する文献レビュー"],"weko_creator_id":"17","weko_shared_id":-1},"updated":"2024-06-18T02:47:00.455996+00:00"}