{"created":"2025-01-19T01:46:07.338659+00:00","updated":"2025-01-19T07:38:48.366490+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00241489","sets":["1164:2735:11468:11810"]},"path":["11810"],"owner":"44499","recid":"241489","title":["MLPモデルのハイパーパラメータ設計問題におけるPSO最適化について"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-12-02"},"_buckets":{"deposit":"e76d5825-49a0-4a6e-8480-abc73b08a681"},"_deposit":{"id":"241489","pid":{"type":"depid","value":"241489","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"MLPモデルのハイパーパラメータ設計問題におけるPSO最適化について","author_link":["664913","664914","664912"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"MLPモデルのハイパーパラメータ設計問題におけるPSO最適化について"},{"subitem_title":"PSO Optimization in Hyper Parameter Optimization Problem of MLP Model","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2024-12-02","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"名古屋大学大学院情報学研究科"},{"subitem_text_value":"名古屋大学大学院情報学研究科"},{"subitem_text_value":"名古屋大学大学院情報学研究科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Informatics, Nagoya University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Informatics, Nagoya University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Informatics, Nagoya 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/241489/files/IPSJ-MPS24151009.pdf","label":"IPSJ-MPS24151009.pdf"},"date":[{"dateType":"Available","dateValue":"2026-12-02"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MPS24151009.pdf","filesize":[{"value":"2.9 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":"e6f243d6-c490-4f28-9bed-c8d1c7cedc20","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"鈴木, 優斗"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"塩見, 健太"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"北, 栄輔"}],"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":"本研究では，多層パーセプトロンモデル (MLP) のハイパーパラメータ最適化手法に粒子群最適化法 (PSO) を適用する．解析例として Wine データセットを使用する．最適化の目的として，予測精度向上だけを目的とする単目的最適化の場合と予測精度向上と最適化の計算コスト減少を目的とする二目的最適化の場合の 2 つを扱う．単目的から二目的にすることで，最良結果における精度は 5～10％ ほど低下したものの，比較手法とほぼ同等の正解率の場合で比較する最適化に要する時間は 30～50％ 削減される．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In this study, particle swarm optimization (PSO) is applied for the hyperparameter optimization problem of a multilayer perceptron model (MLP). Wine dataset is taken as an analysis example. Two optimiation problems are considered. One is singple objective function problem for improving the prediction accuracy and another is two objective functions problem for both improving the prediction accuracy and decreasing the computational cost. Comparing both resuts shows that the accuracy of the best results in two objective functions problem has decreased by about 5 to 10% againt that in single objective function problem, but the computational time is reduced by 30 to 50%.","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":"2024-12-02","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"9","bibliographicVolumeNumber":"2024-MPS-151"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":241489,"links":{}}