{"id":229604,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00229604","sets":["6504:11436:11438"]},"path":["11438"],"owner":"44499","recid":"229604","title":["遺伝的アルゴリズムを用いた因果分析の解釈性の向上"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-02-16"},"_buckets":{"deposit":"466d9887-9b05-4075-90fa-4a7197c9ed4c"},"_deposit":{"id":"229604","pid":{"type":"depid","value":"229604","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"遺伝的アルゴリズムを用いた因果分析の解釈性の向上","author_link":["617601","617602","617604","617603"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"遺伝的アルゴリズムを用いた因果分析の解釈性の向上"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ソフトウェア科学・工学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2023-02-16","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"NECソリューションイノベータ"},{"subitem_text_value":"NECソリューションイノベータ"},{"subitem_text_value":"NECソリューションイノベータ"},{"subitem_text_value":"NECソリューションイノベータ"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/229604/files/IPSJ-Z85-2A-01.pdf","label":"IPSJ-Z85-2A-01.pdf"},"date":[{"dateType":"Available","dateValue":"2023-11-17"}],"format":"application/pdf","filename":"IPSJ-Z85-2A-01.pdf","filesize":[{"value":"528.3 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"2340d48e-d70e-4103-925f-0aadc52add28","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"浅沼, 爽汰"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山本, 純一"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"菅原, 収吾"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"渡部, 佳織"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"　因果分析は、因果の方向を決める因果探索分析と因果を検証する因果推論分析から構成されている．心理尺度などのアンケートを用いた多変数データの因果分析の際に,適合度の良いモデルは因果の数が多く解釈が困難になるという問題がある．そのため，因果の数を減らし解釈を容易にする必要がある.しかし,因果を減らす際に因果のウェイトで閾値を設けて因果を減らすとモデルの適合度が悪化してしまう．そこで,本稿では遺伝的因果探索アルゴリズム(Genetic Causality Discovery：GCD)を使用することで,適合度が良い状態を維持しながら因果を省略する手法を提案する.","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"148","bibliographic_titles":[{"bibliographic_title":"第85回全国大会講演論文集"}],"bibliographicPageStart":"147","bibliographicIssueDates":{"bibliographicIssueDate":"2023-02-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2023"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T11:29:38.804726+00:00","created":"2025-01-19T01:28:51.656126+00:00","links":{}}