{"updated":"2025-01-19T12:25:47.146428+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00226702","sets":["1164:5352:11207:11307"]},"path":["11307"],"owner":"44499","recid":"226702","title":["表形式データを対象とした決定木とニューラルネットワークの融合型機械学習手法の研究"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-06-22"},"_buckets":{"deposit":"4e637717-0d36-4490-8d2c-3ed2c75406ff"},"_deposit":{"id":"226702","pid":{"type":"depid","value":"226702","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"表形式データを対象とした決定木とニューラルネットワークの融合型機械学習手法の研究","author_link":["602561","602555","602557","602560","602556","602558","602559","602562"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"表形式データを対象とした決定木とニューラルネットワークの融合型機械学習手法の研究"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"IBISML","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2023-06-22","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京工業大学AIコンピューティング研究ユニット"},{"subitem_text_value":"東京工業大学AIコンピューティング研究ユニット"},{"subitem_text_value":"東京工業大学AIコンピューティング研究ユニット"},{"subitem_text_value":"東京工業大学AIコンピューティング研究ユニット"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Tokyo Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Tokyo Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Tokyo Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Tokyo Institute of 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/226702/files/IPSJ-BIO23074047.pdf","label":"IPSJ-BIO23074047.pdf"},"date":[{"dateType":"Available","dateValue":"2025-06-22"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-BIO23074047.pdf","filesize":[{"value":"2.5 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Yamakura","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kazushi, Kawamura","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masato, Motomura","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Thiem, Van Chu","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"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_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8590","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本稿では，決定木の構造上の制限をニューラルネットワークに適用した新たな機械学習手法を提案する．提案手法は表形式データを対象とし，3 つの要素で構築されている．1 つ目は，ランダムフォレストを用いた特徴量選択である．既存のランダムフォレストを用いて特徴量選択を行うことで，無意味な特徴量の影響を小さくする． 2 つ目は，木構造ニューラルネットワークである．決定木のように経路を 1 つに絞ることで，ニューラルネットワークの学習パラメータ数を削減する．3 つ目は，決定木とニューラルネットワークのアンサンブル学習である．これは異なる種類の学習器を用いたアンサンブル学習となるため，精度向上につながる．実験の結果，3 つの要素を組み合わせた提案手法は，特徴量の個数が多いデータセットにおいて高精度を達成した．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告バイオ情報学（BIO）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2023-06-22","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"47","bibliographicVolumeNumber":"2023-BIO-74"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:26:01.946811+00:00","id":226702,"links":{}}