{"created":"2025-01-19T01:27:59.494097+00:00","updated":"2025-01-19T11:41:36.366303+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00228857","sets":["934:989:11239:11352"]},"path":["11352"],"owner":"44499","recid":"228857","title":["遺伝的プログラミングと進化的ルール学習を用いた区分的関数同定"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-10-31"},"_buckets":{"deposit":"9ef2bde7-6f3b-4c75-86d8-64d4df25ed63"},"_deposit":{"id":"228857","pid":{"type":"depid","value":"228857","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"遺伝的プログラミングと進化的ルール学習を用いた区分的関数同定","author_link":["614348","614350","614349","614347","614346","614351"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"遺伝的プログラミングと進化的ルール学習を用いた区分的関数同定"},{"subitem_title":"Piecewise Symbolic Regression by Evolutionary Rule-based Learning with Genetic Programming","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[オリジナル論文] 遺伝的プログラミング，進化的ルール学習，区分的関数同定","subitem_subject_scheme":"Other"}]},"item_type_id":"3","publish_date":"2023-10-31","item_3_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"横浜国立大学大学院"},{"subitem_text_value":"横浜国立大学大学院"},{"subitem_text_value":"横浜国立大学大学院"}]},"item_3_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Yokohama National Univercity","subitem_text_language":"en"},{"subitem_text_value":"Yokohama National Univercity","subitem_text_language":"en"},{"subitem_text_value":"Yokohama National Univercity","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/228857/files/IPSJ-TOM1602005.pdf","label":"IPSJ-TOM1602005.pdf"},"date":[{"dateType":"Available","dateValue":"2025-10-31"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-TOM1602005.pdf","filesize":[{"value":"1.3 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":"350b92e0-9d0f-4497-8efe-c2fc76364855","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_3_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"庄子, 天晴"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"栗山, 正輝"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中田, 雅也"}],"nameIdentifiers":[{}]}]},"item_3_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Takaharu, Shoji","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masaki, Kuriyama","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masaya, Nakata","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_3_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11464803","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_3_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7780","subitem_source_identifier_type":"ISSN"}]},"item_3_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本論文では，進化的ルール学習と遺伝的プログラミングを組み合わせた区分的関数同定手法を提案する．進化的ルール学習が入力空間の部分領域ごとに特化した分類器（ルール）を生成できるという利点に着目し，これを不連続かつ非線形な関数を対象とする関数同定問題へ拡張することで，区分ごとに同定モデルを獲得可能な方法を構築する．提案手法では，区分範囲とその区分に適用する同定モデルの組から構成されるルールを進化的に最適化する．また，ルールの探索空間が増加し探索効率が低下するという問題点を緩和するために，ルールが示す区分範囲の修復メカニズムも導入する．実験結果では，1次元ならびに2次元の不連続関数に対し，提案手法が不連続点を検出し，区分ごとに真の関数を同定できることを示す．以上より，区分的関数同定手法としての進化的ルール学習の実現可能性を初めて明らかにする．","subitem_description_type":"Other"}]},"item_3_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"This paper presents an evolutionary rule-based learning (ERL) method adapted for piecewise symbolic regression tasks, wherein target functions to be regressed typically involve the discontinuity and non-linearity. The presented approach is to integrate a genetic programming (GP) approach into ERL, and evolutionary algorithms optimize a set of IF-THEN rules, wherein each rule corresponds to an independent piecewise function described with a GP representation. Further, to tackle the complexity behind this approach, that is, the increase of the search space of rules, we introduce a rule-repair algorithm, aiming to boost the rule-search capacity of our algorithm. Experimental results show that the proposed algorithm can obtain proper discontinuous, nonlinear functions with up to 2 variables. Thus, to best of our knowledge, the main contribution of this paper is, for the first time, to demonstrate the potential of ERL on the piecewise symbolic regression tasks.","subitem_description_type":"Other"}]},"item_3_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"49","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌数理モデル化と応用（TOM）"}],"bibliographicPageStart":"36","bibliographicIssueDates":{"bibliographicIssueDate":"2023-10-31","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicVolumeNumber":"16"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":228857,"links":{}}