{"id":58896,"updated":"2025-01-22T03:49:36.227014+00:00","links":{},"created":"2025-01-18T23:21:46.842457+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00058896","sets":["1164:5352:5353:5357"]},"path":["5357"],"owner":"1","recid":"58896","title":["Super-Structure : A Decisive Framework to Rationalize and Improve Both Optimal and Heuristic Bayesian Network Structure Learning Algorithms"],"pubdate":{"attribute_name":"公開日","attribute_value":"2008-03-03"},"_buckets":{"deposit":"83be2379-5575-4134-924f-98ebd0f925f2"},"_deposit":{"id":"58896","pid":{"type":"depid","value":"58896","revision_id":0},"owners":[1],"status":"published","created_by":1},"item_title":"Super-Structure : A Decisive Framework to Rationalize and Improve Both Optimal and Heuristic Bayesian Network Structure Learning Algorithms","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Super-Structure : A Decisive Framework to Rationalize and Improve Both Optimal and Heuristic Bayesian Network Structure Learning Algorithms"},{"subitem_title":"Super-Structure : A Decisive Framework to Rationalize and Improve Both Optimal and Heuristic Bayesian Network Structure Learning Algorithms","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2008-03-03","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京大学医科学研究所ヒトゲノム解析センター"},{"subitem_text_value":"東京大学医科学研究所ヒトゲノム解析センター"},{"subitem_text_value":"東京大学医科学研究所ヒトゲノム解析センター"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Human Genome Center, Institute of Medical Science, University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"Human Genome Center, Institute of Medical Science, University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"Human Genome Center, Institute of Medical Science, University of Tokyo","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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/58896/files/IPSJ-BIO08012003.pdf"},"date":[{"dateType":"Available","dateValue":"2010-03-03"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-BIO08012003.pdf","filesize":[{"value":"683.3 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"41"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"8545a8d2-4143-4843-a0ea-66cf1197cef5","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2008 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Eric, Perrier"},{"creatorName":"井元, 清哉"},{"creatorName":"宮野, 悟"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Eric, Perrier","creatorNameLang":"en"},{"creatorName":"Seiya, Imoto","creatorNameLang":"en"},{"creatorName":"Satoru, Miyano","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_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"データに基づくベイジアンネットワークの構造学習において精度向上は，遺伝子ネットワークのような巨大システムをモデル化する際に極めて重要となる．この目的のため，我々は，スコア関数に基づく探索に無向グラフによるsuper-structure を制約として用いる方法を提案する．つまり，探索する枝はsuper-structure に含まれているものに限る．さらに，データを用いた super-structure の近似法を導入し，制約付き最適アルゴリズム(COS) を構成する．COS により，最適アルゴリズムはより大きなネットワークに適用可能となる．また，トポロジカル順序に基づく発見的アルゴリズム (PERM) を導出できる．数値実験により，提案するアルゴリズムは，他の方法よりも精度の面で優れていることを示した．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Improving the accuracy of Bayesian network learning from data is a decisive challenge to model huge systems such as genes networks. With this end, we propose to constraint the scoring function based algorithms with a super-structure encoded by an undirected graph. This restricts the possible edges of the networks to the ones it contains. Further, we introduce a basic method to approximate a super-structure from data. Then, we develop a constrained optimal search (COS) that extends exact algorithms to sensitively bigger graphs, and a heuristic hill climbing over topological orderings (PERM). Experimentally, these algorithms outperform significantly other approaches.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"24","bibliographic_titles":[{"bibliographic_title":"情報処理学会研究報告バイオ情報学（BIO）"}],"bibliographicPageStart":"17","bibliographicIssueDates":{"bibliographicIssueDate":"2008-03-03","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"15(2008-BIO-012)","bibliographicVolumeNumber":"2008"}]},"relation_version_is_last":true,"weko_creator_id":"1"}}