{"links":{},"id":17062,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00017062","sets":["934:989:990:991"]},"path":["991"],"owner":"1","recid":"17062","title":["特徴抽出方法の改善によるベイジアンフィルタの精度向上"],"pubdate":{"attribute_name":"公開日","attribute_value":"2008-09-26"},"_buckets":{"deposit":"6da33e56-f36c-4f71-990f-62fc61e1e7c7"},"_deposit":{"id":"17062","pid":{"type":"depid","value":"17062","revision_id":0},"owners":[1],"status":"published","created_by":1},"item_title":"特徴抽出方法の改善によるベイジアンフィルタの精度向上","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"特徴抽出方法の改善によるベイジアンフィルタの精度向上"},{"subitem_title":"Improvement of Feature Extraction for Bayesian Spam Filtering","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"システム開発論文","subitem_subject_scheme":"Other"}]},"item_type_id":"3","publish_date":"2008-09-26","item_3_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"株式会社ジャストシステム"},{"subitem_text_value":"株式会社ジャストシステム"},{"subitem_text_value":"信州大学工学部情報工学科"}]},"item_3_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"JustSystems Corporation","subitem_text_language":"en"},{"subitem_text_value":"JustSystems Corporation","subitem_text_language":"en"},{"subitem_text_value":"Department of Information Engineering, Faculty of Engineering, Shinshu 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/17062/files/IPSJ-TOM0101015.pdf"},"date":[{"dateType":"Available","dateValue":"2010-09-26"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-TOM0101015.pdf","filesize":[{"value":"554.4 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":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"5a394401-1a24-4a71-951b-059f92fc7dad","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2008 by the Information Processing Society of Japan"}]},"item_3_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"谷岡, 広樹"},{"creatorName":"中川, 尚"},{"creatorName":"丸山, 稔"}],"nameIdentifiers":[{}]}]},"item_3_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Hiroki, Tanioka","creatorNameLang":"en"},{"creatorName":"Takashi, Nakagawa","creatorNameLang":"en"},{"creatorName":"Minoru, Maruyama","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":"本稿では，従来法の1つであるベイジアンフィルタを用いたspamメールフィルタの精度（true negative rate）を改善する方法について提案する．これまでの学習型spamメールフィルタとしては，ベイジアンフィルタがよく利用されており，一定の成果が得られている．しかしながら，ベイジアンフィルタを利用した方法においても，誤検出率（false positive rate）の低減や，さらなる精度向上が期待される．我々は，単語のspam確率（尤度）の分布およびメールのspam度の分布状況を分析し，誤検出をおさえながらも，高い判定精度を実現する方法について提案し，その精度について，従来方式と比較して評価する．","subitem_description_type":"Other"}]},"item_3_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"We propose an improved baysian filter for spam mail detection. Bayesian filter was used on existing learning spam filters which achieved some positive results. Although we expect them to improve the true negative rate while keeping the false positive rate low. Therefore, it was based on a thorough review of distribution for each word and mail that our means of spam mail detection showed an impressively higher accuracy than ever.","subitem_description_type":"Other"}]},"item_3_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"184","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌数理モデル化と応用（TOM）"}],"bibliographicPageStart":"175","bibliographicIssueDates":{"bibliographicIssueDate":"2008-09-26","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"1"}]},"relation_version_is_last":true,"weko_creator_id":"1"},"created":"2025-01-18T22:50:09.695949+00:00","updated":"2025-01-22T23:33:33.244129+00:00"}