{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00233679","sets":["1164:10193:11470:11538"]},"path":["11538"],"owner":"44499","recid":"233679","title":["疑似量子アニーリングによる金融取引データのオーバーサンプリングの有効性検証"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-03-21"},"_buckets":{"deposit":"20718943-04d5-4cb1-b829-d05143a65fd7"},"_deposit":{"id":"233679","pid":{"type":"depid","value":"233679","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"疑似量子アニーリングによる金融取引データのオーバーサンプリングの有効性検証","author_link":["635283","635276","635282","635272","635281","635279","635277","635280","635278","635273","635274","635275"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"疑似量子アニーリングによる金融取引データのオーバーサンプリングの有効性検証"},{"subitem_title":"Validations of oversampling data in credit card fraud detection using quantum-inspired annealing","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2024-03-21","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"株式会社日本総合研究所"},{"subitem_text_value":"株式会社日本総合研究所"},{"subitem_text_value":"日本電気株式会社"},{"subitem_text_value":"日本電気株式会社"},{"subitem_text_value":"日本電気株式会社"},{"subitem_text_value":"日本電気株式会社"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"The Japan Research Institute, Limited","subitem_text_language":"en"},{"subitem_text_value":"The Japan Research Institute, Limited","subitem_text_language":"en"},{"subitem_text_value":"NEC Corporation","subitem_text_language":"en"},{"subitem_text_value":"NEC Corporation","subitem_text_language":"en"},{"subitem_text_value":"NEC Corporation","subitem_text_language":"en"},{"subitem_text_value":"NEC Corporation","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/233679/files/IPSJ-QS24011005.pdf","label":"IPSJ-QS24011005.pdf"},"date":[{"dateType":"Available","dateValue":"2026-03-21"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-QS24011005.pdf","filesize":[{"value":"884.4 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暢達"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Ayako, Fujimura","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yoshihiro, Mino","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Sho, Shibata","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Haruna, Imai","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takumi, Okazawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Nobutatsu, Nakamura","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12894105","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":"2435-6492","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"クレジットカード等金融取引のセキュリティ対策において，ルールベースの不正検知や機械学習を活用した不正検知システムが導入されつつある．しかし，不正取引データは正常な取引データに比べ件数が少ないために，機械学習モデル構築に利用する学習データの量として十分でないことが問題となっている．量子アニーリングを用いた制限ボルツマンマシンによって不正データに相当する学習データを生成する手法は，従来のオーバーサンプリング方式や補間サンプリング方式よりも確からしい学習データを増量させることができるため，不正検知の精度が向上する．本報告では，実業務で得られた金融取引データに対し，疑似量子アニーリングを使ったオーバーサンプリングを適用する検証を行い，その有効性を確認した．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"9","bibliographic_titles":[{"bibliographic_title":"研究報告量子ソフトウェア（QS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-03-21","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"5","bibliographicVolumeNumber":"2024-QS-11"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":233679,"updated":"2025-01-19T10:01:32.837979+00:00","links":{},"created":"2025-01-19T01:35:11.913885+00:00"}