{"created":"2025-01-19T01:29:23.924702+00:00","updated":"2025-01-19T11:21:18.255187+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00229938","sets":["6504:11436:11440"]},"path":["11440"],"owner":"44499","recid":"229938","title":["CorrectandSmoothを用いたアンサンブル手法に関する一考察"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-02-16"},"_buckets":{"deposit":"cacbf806-42c3-4931-b4a7-c8363e3bce8b"},"_deposit":{"id":"229938","pid":{"type":"depid","value":"229938","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"CorrectandSmoothを用いたアンサンブル手法に関する一考察","author_link":["618587","618585","618586"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"CorrectandSmoothを用いたアンサンブル手法に関する一考察"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"人工知能と認知科学","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":"湘南工科大"},{"subitem_text_value":"東京都市大"},{"subitem_text_value":"湘南工科大"}]},"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/229938/files/IPSJ-Z85-7Q-08.pdf","label":"IPSJ-Z85-7Q-08.pdf"},"date":[{"dateType":"Available","dateValue":"2023-11-17"}],"format":"application/pdf","filename":"IPSJ-Z85-7Q-08.pdf","filesize":[{"value":"406.8 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"bfcd100d-a694-4a8e-857f-87d5d7e2f59e","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":"石川, 悠樹"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"三川, 健太"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"二宮, 洋"}],"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":"近年，グラフ構造を持つデータセットに対する機械学習手法が注目されており，その一手法としてCorrect and Smoothと呼ばれる手法が存在する．C&Sは軟判定が可能な任意の分類器を用いた初期予測を行い，それを基に2段階のラベル伝播を行うことで少ない学習コストで高い性能を実現している．本研究では，このC&Sに対するアンサンブルを行うことで分類精度を向上させるための方法について検討を行う．具体的にはC&Sの2段階のラベル伝播がもたらす効果に注目し，個別の分類器の精度を1段階目の伝搬で修正し，統合した予測を平滑化することで高精度の手法を提案する．また，提案手法の有効性を示すため，ベンチマークデータを用いたシミュレーション実験により，提案手法の有効性について示す．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"282","bibliographic_titles":[{"bibliographic_title":"第85回全国大会講演論文集"}],"bibliographicPageStart":"281","bibliographicIssueDates":{"bibliographicIssueDate":"2023-02-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2023"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":229938,"links":{}}