{"links":{},"id":227631,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00227631","sets":["1164:2735:11166:11323"]},"path":["11323"],"owner":"44499","recid":"227631","title":["潜在クラスに基づく混合線形回帰モデルを用いたUplift Modelingのベイズ最適な予測とその近似アルゴリズム"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-08-31"},"_buckets":{"deposit":"c94d0b83-a243-4538-9406-1fdecfa9ec13"},"_deposit":{"id":"227631","pid":{"type":"depid","value":"227631","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"潜在クラスに基づく混合線形回帰モデルを用いたUplift Modelingのベイズ最適な予測とその近似アルゴリズム","author_link":["606582","606581"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"潜在クラスに基づく混合線形回帰モデルを用いたUplift Modelingのベイズ最適な予測とその近似アルゴリズム"}]},"item_type_id":"4","publish_date":"2023-08-31","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"早稲田大学基幹理工学部"},{"subitem_text_value":"早稲田大学基幹理工学部"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Waseda University","subitem_text_language":"en"},{"subitem_text_value":"Waseda University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/227631/files/IPSJ-MPS23145002.pdf","label":"IPSJ-MPS23145002.pdf"},"date":[{"dateType":"Available","dateValue":"2025-08-31"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MPS23145002.pdf","filesize":[{"value":"1.1 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":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"5ac2f739-a9b2-41ef-b9e1-52fe80732e6b","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"飯窪, 祐二"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"松嶋, 敏泰"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10505667","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8833","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"Uplift modeling は，母集団からサンプリングされた個体に対して，何らかの処置を行った場合と行わなかった場合の差の予測を行う手法の一つであり，マーケティングや個別化医療など様々な分野に応用されている．本研究では，回帰問題における Uplift modeling に対して，潜在クラスごとに異なる線形回帰モデルが割り当てられた確率モデルによるモデル化を提案する．また提案したモデルに対して，二乗誤差損失におけるベイズリスク関数を最小にするという評価基準のもとで，最適な決定関数の導出を行う．しかし，導出された最適な決定関数を解析的に求めることは一般的に困難である．そこで本研究では，特定の事前分布を仮定したもとでの変分ベイズ法に基づく近似計算アルゴリズムを提案する．提案アルゴリズムについて，人工データおよび半人工データを用いて数値実験を行い，その有効性を検証する．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告数理モデル化と問題解決（MPS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2023-08-31","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicVolumeNumber":"2023-MPS-145"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:26:53.275601+00:00","updated":"2025-06-30T02:30:08.122072+00:00"}