{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00218601","sets":["1164:2735:10865:10962"]},"path":["10962"],"owner":"44499","recid":"218601","title":["大規模データに基づく機会制約問題に対する機械学習と進化計算を用いた近似解法"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-06-20"},"_buckets":{"deposit":"adf290be-9250-4585-9679-ee9e094bbaae"},"_deposit":{"id":"218601","pid":{"type":"depid","value":"218601","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"大規模データに基づく機会制約問題に対する機械学習と進化計算を用いた近似解法","author_link":["568912"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"大規模データに基づく機会制約問題に対する機械学習と進化計算を用いた近似解法"},{"subitem_title":"Approximate Method using Machine Learning and Evolutionary Algorithm for Large-Scale Data-Based Chance Constrained Problems","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":"近畿大学情報学部"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Faculty of Informatics, Kindai 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/218601/files/IPSJ-MPS22138031.pdf","label":"IPSJ-MPS22138031.pdf"},"date":[{"dateType":"Available","dateValue":"2024-06-20"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MPS22138031.pdf","filesize":[{"value":"1.0 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":"4e7411de-8bff-408c-be86-1e9bbb7b415f","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"田川, 聖治"}],"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":"本稿では，従来の確率論的な機会制約問題（CCP）を，大規模データに基づく決定論的な最適化問題に定式化する．決定論的 CCP では，指定された充足水準で制約条件を満たすか否かで，解の実行可能性を判定するだけでなく，与えられた全データを 2 クラスに分類する．次に，サポートベクトルマシン（SVM）と進化計算アルゴリズムを組合せた決定論的 CCP の近似解法を提案する．さらに，上記の用途に適した SVM の教師データの作成法として，主成分分析を利用した層化抽出法を紹介する．最後に，教師データの違いによる SVM の性能を評価した後，決定論的 CCP の例題により近似解法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":"2022-06-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"31","bibliographicVolumeNumber":"2022-MPS-138"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":218601,"updated":"2025-01-19T15:06:46.045446+00:00","links":{},"created":"2025-01-19T01:18:57.406543+00:00"}