{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00218639","sets":["1164:5352:10882:10963"]},"path":["10963"],"owner":"44499","recid":"218639","title":["順序回帰のための全変動正則化付き加法累積ロジットモデル"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-06-20"},"_buckets":{"deposit":"6f640f05-5e4c-4ff5-b9a3-a67f9278d576"},"_deposit":{"id":"218639","pid":{"type":"depid","value":"218639","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"順序回帰のための全変動正則化付き加法累積ロジットモデル","author_link":["569076","569077","569075","569074"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"順序回帰のための全変動正則化付き加法累積ロジットモデル"},{"subitem_title":"Additive Cumulative Link Model with Total Variation Regularization for Ordinal Regression","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2022-06-20","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京大学情報理工学研究科"},{"subitem_text_value":" 東京大学総合文化研究科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information Science and Technology, The University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Arts and Sciences,The University of Tokyo","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/218639/files/IPSJ-BIO22070009.pdf","label":"IPSJ-BIO22070009.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-BIO22070009.pdf","filesize":[{"value":"1.1 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"41"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"3386b6f4-5949-437a-8dea-79631037bf47","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"伊従, 寛哉"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"松島, 慎"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Hiroya, Iyori","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Shin, Matsushima","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12055912","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":"2188-8590","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"医学研究や社会科学などの実質科学分野ではデータが順序尺度で得られることが少なくない．目的変数がこのような順序尺度で与えられるような問題を順序回帰とよび，回帰問題とも分類問題とも違った特徴を持つ．順序回帰問題の教師あり学習においては，未知のデータに対する予測性能の高さとともに，学習したモデルが解釈性を持つことも重要である．本稿では解釈性と予測性能の両方に優れた全変動正則化付きの加法モデルを順序回帰問題に対して拡張し，予測性能と解釈性の両方に優れている全変動正則化付き加法累積ロジットモデル (TVACLM) を提案する．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In many fields such as medical research and social science, data on an ordinal scale are often obtained. Problems in which the target variable is given on the ordinal scale are called ordinal regression. Ordinal regression has different characteristics from those of regression and classification problems. In supervised learning of the ordinal regression problems, interpretability of the learned model is very important as well as its predictive performance. In this paper, we extend the generalized additive model with total variation regularization to ordinal regression problems and propose a additive cumulative logit model with total varition regularization (TVACLM) that achieves good performance in both perspectives from interpretability and prediction.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"7","bibliographic_titles":[{"bibliographic_title":"研究報告バイオ情報学（BIO）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-06-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"9","bibliographicVolumeNumber":"2022-BIO-70"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":218639,"updated":"2025-01-19T15:05:54.862325+00:00","links":{},"created":"2025-01-19T01:18:59.644870+00:00"}