{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00021154","sets":["1164:1384:1390:1394"]},"path":["1394"],"owner":"1","recid":"21154","title":["Fault-Proneモジュール判別モデルに対する外れ値除去法の適用効果"],"pubdate":{"attribute_name":"公開日","attribute_value":"2007-03-22"},"_buckets":{"deposit":"328cfe68-812e-48ff-9923-885330ac1b88"},"_deposit":{"id":"21154","pid":{"type":"depid","value":"21154","revision_id":0},"owners":[1],"status":"published","created_by":1},"item_title":"Fault-Proneモジュール判別モデルに対する外れ値除去法の適用効果","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Fault-Proneモジュール判別モデルに対する外れ値除去法の適用効果"},{"subitem_title":"Comparison of Outlier Detection Methods in Fault-Prone Module Detection Models ","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2007-03-22","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"奈良先端科学技術大学院大学情報科学研究科"},{"subitem_text_value":"奈良先端科学技術大学院大学情報科学研究科"},{"subitem_text_value":"奈良先端科学技術大学院大学情報科学研究科"},{"subitem_text_value":"奈良先端科学技術大学院大学情報科学研究科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information Science, Nara Institute of Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science, Nara Institute of Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science, Nara Institute of Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science, Nara Institute of Science and Technology","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/21154/files/IPSJ-SE07155007.pdf"},"date":[{"dateType":"Available","dateValue":"2009-03-22"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-SE07155007.pdf","filesize":[{"value":"1.1 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":"12"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"dae62e74-cb90-48f7-9b4a-8028a3d4f1b3","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2007 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"柏本真佑"},{"creatorName":"亀井, 靖高"},{"creatorName":"門田暁人"},{"creatorName":"松本, 健一"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"SHINSUKE, MATSUMOTO","creatorNameLang":"en"},{"creatorName":"YASUTAKA, KAMEI","creatorNameLang":"en"},{"creatorName":"AKITO, MONDEN","creatorNameLang":"en"},{"creatorName":"KEN-ICHI, MATSUMOTO","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10112981","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":"本稿ではfault-proneモジュール判別モデルに対する外れ値除去法の効果を実験的に明らかにする．外れ値除去法とは，標本の中から外れ値を検出・除去する方法であり，判別モデル構築の前処理に用いることができる．実験では３つの代表的な判別モデル（線形判別分析，ロジステイック回帰分析，分類木）に対して，２つの外れ値除去法(ＭＯＡ，ＬＯＦＭ）を適用した場合の計６通りについて比較を行った．実験の結果，外れ値除去法を適用しない場合と比べて，ＭＯＡを適用した場合は，Ｆ１値が最小0.04，最大0.17 平均０．１０向上し，全ての判別モデルにおいて判別精度が向上した．一方，LOFMを適用した場合は，最小-001，最大004 平均0.01変化し，判別モデルによってはＦ１値が向上したものの，その幅はＭＯＡと比べて小さかった．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In this paper, we experimentally evaluate outlier detection methods, which detect data points that are far away from others in a data set, in terms of improving the pre diction performance of fault-prone module detection models. In the experiment, we compared two outlier detection methods (MOA, LOFM) each applied to three well-known fault-prone module detection models (LDA, LRA, CT). The result showed that MOA improved Fl-values of all fault-proneness models (0.04 at minimum, 0.17 at maximum and 0.10 at mean) while improvements by LOFM were relatively small (-0.01 at minimum, 0.04 at maximum and 0.01 at mean).","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"56","bibliographic_titles":[{"bibliographic_title":"情報処理学会研究報告ソフトウェア工学（SE） "}],"bibliographicPageStart":"49","bibliographicIssueDates":{"bibliographicIssueDate":"2007-03-22","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"33(2007-SE-155)","bibliographicVolumeNumber":"2007"}]},"relation_version_is_last":true,"weko_creator_id":"1"},"updated":"2025-01-22T21:27:47.280952+00:00","created":"2025-01-18T22:53:10.608040+00:00","links":{},"id":21154}