{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00218653","sets":["1164:5352:10882:10963"]},"path":["10963"],"owner":"44499","recid":"218653","title":["予測モデルにおけるLiNGAMを用いた特徴量選択"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-06-20"},"_buckets":{"deposit":"03a78175-d8c4-4bc1-8128-9e96daa62075"},"_deposit":{"id":"218653","pid":{"type":"depid","value":"218653","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"予測モデルにおけるLiNGAMを用いた特徴量選択","author_link":["569149","569148","569147","569150"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"予測モデルにおけるLiNGAMを用いた特徴量選択"},{"subitem_title":"Feature selection in prediction model by LiNGAM","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2022-06-20","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"工学院大学情報学部"},{"subitem_text_value":"工学院大学情報学部"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Faculty of Informatics, Kogakuin University of Technology & Engineering","subitem_text_language":"en"},{"subitem_text_value":"Faculty of Informatics, Kogakuin University of Technology & Engineering","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/218653/files/IPSJ-BIO22070023.pdf","label":"IPSJ-BIO22070023.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-BIO22070023.pdf","filesize":[{"value":"967.1 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"41"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"fddf4029-f218-4c37-84ea-2d6e7ed53de9","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"隅田, 大勇"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"竹川, 高志"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Taiyu, Sumida","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takashi, Takekawa","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_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":"機械学習モデルの精度を向上させるためには，使用するモデルの特徴，およびデータのドメイン知識を踏まえたうえで特徴量エンジニアリングを行うことが重要である．機械学習モデルを構築する者がデータのドメイン知識を深く理解していない場合は，関連する知見の調査が必要である．そもそも得られたデータの変数間の因果関係が不明である場合は，詳細な探索的データ分析を行う必要がある．本研究ではデータからそのデータの変数間の因果グラフを推定する統計的因果探索の手法である LiNGAM を用いた特徴量選択と交互作用項の生成を組み合わせることによって，予測モデルの精度を向上させることができた．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"To improve the accuracy of machine learning models, it is important to perform feature engineering based on the features of the model used and the domain knowledge of the data. If the person building the machine learning model does not have a deep understanding of the domain knowledge of the data, a survey of relevant knowledge is necessary. If the causal relationships between variables in the obtained data are unknown to begin with, detailed exploratory data analysis should be conducted. In this study, we were able to improve the accuracy of the prediction model by combining feature selection and interaction term generation using LiNGAM, a statistical causal discovery method that estimates the causal graph between variables in the data.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告バイオ情報学（BIO）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-06-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"23","bibliographicVolumeNumber":"2022-BIO-70"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":218653,"updated":"2025-01-19T15:05:39.173467+00:00","links":{},"created":"2025-01-19T01:19:00.454824+00:00"}