{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00219579","sets":["6164:6165:6640:11008"]},"path":["11008"],"owner":"44499","recid":"219579","title":["コンセプトドリフト対処のための，Adversarial Validationを用いた学習データ選択アルゴリズムの方式検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-07-06"},"_buckets":{"deposit":"93659e3c-92f5-4737-b8a4-f79a1b94833d"},"_deposit":{"id":"219579","pid":{"type":"depid","value":"219579","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"コンセプトドリフト対処のための，Adversarial Validationを用いた学習データ選択アルゴリズムの方式検討","author_link":["572826","572825","572824"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"コンセプトドリフト対処のための，Adversarial Validationを用いた学習データ選択アルゴリズムの方式検討"}]},"item_type_id":"18","publish_date":"2022-07-06","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"お茶の水女子大学"},{"subitem_text_value":"津田塾大学"},{"subitem_text_value":"お茶の水女子大学"}]},"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/219579/files/IPSJ-DICOMO2022005.pdf","label":"IPSJ-DICOMO2022005.pdf"},"date":[{"dateType":"Available","dateValue":"2024-07-06"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-DICOMO2022005.pdf","filesize":[{"value":"4.8 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"295fcb0a-ddb1-4bc7-b886-0021b1d697bb","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_18_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_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"機械学習モデルの利活用が進み，あるタスクを行うモデルが長期的に利用されるシナリオが想定されるようになった．しかし，長期的な機械学習モデル活用の問題点として，コンセプトドリフトなどが原因の精度低下がある．我々はこれまでに，大規模ストリームデータにおいて時間経過などによりコンセプトドリフトが発生する場面で特定のタスクを行う教師あり学習モデルを継続的に入手し，自動的に時系列データの予測を行う枠組みを提案した．本研究は，その枠組みの根幹を担う敵対的分類器でデータ選択を行うことの効果について，シンセティックなデータを用いて考察を行う．","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"35","bibliographic_titles":[{"bibliographic_title":"マルチメディア，分散，協調とモバイルシンポジウム2022論文集"}],"bibliographicPageStart":"28","bibliographicIssueDates":{"bibliographicIssueDate":"2022-07-06","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"2022"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":219579,"updated":"2025-01-19T14:50:41.318311+00:00","links":{},"created":"2025-01-19T01:19:39.056603+00:00"}