{"id":215114,"updated":"2025-01-19T16:18:59.128830+00:00","links":{},"created":"2025-01-19T01:15:53.658839+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00215114","sets":["6504:10735:10807"]},"path":["10807"],"owner":"44499","recid":"215114","title":["時系列生体情報を用いた回帰問題のためのデータバランシング手法の提案"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-03-04"},"_buckets":{"deposit":"7b7ae62f-2041-4854-b132-5cd6f87cce79"},"_deposit":{"id":"215114","pid":{"type":"depid","value":"215114","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"時系列生体情報を用いた回帰問題のためのデータバランシング手法の提案","author_link":["553702","553701","553703"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"時系列生体情報を用いた回帰問題のためのデータバランシング手法の提案"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ネットワーク","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2021-03-04","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":"阪大"},{"subitem_text_value":"阪大"},{"subitem_text_value":"阪大"}]},"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/215114/files/IPSJ-Z83-2D-07.pdf","label":"IPSJ-Z83-2D-07.pdf"},"date":[{"dateType":"Available","dateValue":"2021-12-29"}],"format":"application/pdf","filename":"IPSJ-Z83-2D-07.pdf","filesize":[{"value":"247.2 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"21f9a059-42a5-491c-8728-1de4b9b2d492","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"吉川, 寛樹"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"内山, 彰"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"東野, 輝夫"}],"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":"生体情報を入力とし，人の心理状態や身体内部の状態を推定する問題において機械学習を用いることの有効性が多くの研究により示されている．しかしながら，特に生体情報は分布の偏りなく多くのデータを収集することが倫理的，物理的に難しい場合があり，特に異常時のデータは平常時のデータと比較して量が少なくなる．この結果発生する分布の偏りは，機械学習においてオーバーフィッティングの原因となる．本研究では，時系列生体情報を含むデータセットから連続値を推定する回帰問題において，データ生成により分布の偏りを解消する手法を提案する．評価のため温冷感や深部体温を正解ラベルとしたデータセットに対し，手法の有効性を確認した．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"26","bibliographic_titles":[{"bibliographic_title":"第83回全国大会講演論文集"}],"bibliographicPageStart":"25","bibliographicIssueDates":{"bibliographicIssueDate":"2021-03-04","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2021"}]},"relation_version_is_last":true,"weko_creator_id":"44499"}}