{"created":"2025-01-19T01:29:22.912325+00:00","updated":"2025-01-19T11:21:33.205921+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00229927","sets":["6504:11436:11440"]},"path":["11440"],"owner":"44499","recid":"229927","title":["データドリフト対処のためのAdversarial Validationを用いたデータ選択指標の評価"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-02-16"},"_buckets":{"deposit":"f2eebc9b-e98b-4bef-b53c-8fb86f7e545d"},"_deposit":{"id":"229927","pid":{"type":"depid","value":"229927","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"データドリフト対処のためのAdversarial Validationを用いたデータ選択指標の評価","author_link":["618549","618550","618548"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"データドリフト対処のためのAdversarial Validationを用いたデータ選択指標の評価"}]},"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":"お茶の水女子大"},{"subitem_text_value":"津田塾大"},{"subitem_text_value":"お茶の水女子大"}]},"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/229927/files/IPSJ-Z85-6Q-06.pdf","label":"IPSJ-Z85-6Q-06.pdf"},"date":[{"dateType":"Available","dateValue":"2023-11-17"}],"format":"application/pdf","filename":"IPSJ-Z85-6Q-06.pdf","filesize":[{"value":"437.4 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"06cebe35-0a73-4154-a45c-30b82f22bdc9","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":"今野, 由麻"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中野, 美由紀"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小口, 正人"}],"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":"Adversarial Validationは，機械学習において学習データとテストデータの分布の違いを検出する手法であり，より性能の良いモデルを学習するために用いられる．先行研究として，Adversarial Validationをデータドリフト対処のために利用し，教師あり機械学習モデルを更新して時系列データの予測をバッチ単位で行う枠組みが提案されている．その特徴量選択に注目した先行研究の枠組みを応用して，我々はこれまでにデータ選択を行う枠組みを提案してきた．本研究では，データ選択手法で利用するデータの選択指標について，人工ドリフトデータセットを用いた検討を行う．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"260","bibliographic_titles":[{"bibliographic_title":"第85回全国大会講演論文集"}],"bibliographicPageStart":"259","bibliographicIssueDates":{"bibliographicIssueDate":"2023-02-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2023"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":229927,"links":{}}