{"id":10521,"updated":"2025-01-23T02:48:17.034510+00:00","links":{},"created":"2025-01-18T22:45:24.504515+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00010521","sets":["581:625:628"]},"path":["628"],"owner":"1","recid":"10521","title":["獲得した情報を用いる遺伝的ネットワークプログラミングによるデータマイニング"],"pubdate":{"attribute_name":"公開日","attribute_value":"2005-10-15"},"_buckets":{"deposit":"9d9f888a-0eee-498f-a520-507736f8a2e6"},"_deposit":{"id":"10521","pid":{"type":"depid","value":"10521","revision_id":0},"owners":[1],"status":"published","created_by":1},"item_title":"獲得した情報を用いる遺伝的ネットワークプログラミングによるデータマイニング","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"獲得した情報を用いる遺伝的ネットワークプログラミングによるデータマイニング"},{"subitem_title":"Data Mining Using Genetic Network Programming with the Use of Acquired Information","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"論文","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2005-10-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"早稲田大学大学院情報生産システム研究科"},{"subitem_text_value":"早稲田大学大学院情報生産システム研究科"},{"subitem_text_value":"早稲田大学大学院情報生産システム研究科"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information, Production and Systems, Waseda University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information, Production and Systems, Waseda University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information, Production and Systems, Waseda University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/10521/files/IPSJ-JNL4610021.pdf"},"date":[{"dateType":"Available","dateValue":"2007-10-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL4610021.pdf","filesize":[{"value":"535.6 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":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"4c2f2c93-97e9-421d-b066-37f1e540f86b","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2005 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"嶋田, 香"},{"creatorName":"平澤, 宏太郎"},{"creatorName":"古月, 敬之"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Kaoru, Shimada","creatorNameLang":"en"},{"creatorName":"Kotaro, Hirasawa","creatorNameLang":"en"},{"creatorName":"Takayuki, Furuzuki","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","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_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"遺伝的ネットワークプログラミング（Genetic Network Programming，GNP）を用いた興味深い相関ルールの抽出法を提案する．統計学で用いられるχ2値を指標の一部とした興味深い相関ルールを進化論的計算手法によって抽出する．相関ルールの指標はGNPの構造的な特徴を利用して算出される．ルール抽出は世代継続的に行われるため抽出された相関ルールはライブラリに蓄積される．抽出された相関ルールに関する情報は，抽出を継続中のGNPの個体評価および進化操作時に用いられる．したがって，本手法は通常の進化論的計算手法とは進化の方法が異なる．シミュレーション結果から，本手法が興味深い相関ルールの抽出を効率的に行うことが示された．","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"A method of association rule mining using Genetic Network Programming (GNP) is proposed to improve the performance of rule extraction. The proposed system evolves itself by an evolutionary method and measures the significance of the association via the chi-squared test using GNP. Extracted association rules are stored in a pool all together through generations in order to find new important rules. These rules are reflected in genetic operators as acquired information. Therefore, the proposed method is fundamentally different from all other evolutionary methods in its evolutionary way. In this paper, we describe the algorithm capable of finding the important association rules and present some experimental results.","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"2586","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicPageStart":"2576","bibliographicIssueDates":{"bibliographicIssueDate":"2005-10-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"10","bibliographicVolumeNumber":"46"}]},"relation_version_is_last":true,"item_2_alternative_title_2":{"attribute_name":"その他タイトル","attribute_value_mlt":[{"subitem_alternative_title":"知識処理"}]},"weko_creator_id":"1"}}