Kernel Logistic Regression based on the Confusion Matrix for Imbalanced Data Classification

• Item type: SIG Technical Reports(1)
• 公開日: 2015-06-16
• タイトル: Kernel Logistic Regression based on the Confusion Matrix for Imbalanced Data Classification
• タイトル / 言語: en
• タイトル: Kernel Logistic Regression based on the Confusion Matrix for Imbalanced Data Classification
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Doshisha University
• 著者所属: Doshisha University
• 著者所属: Doshisha University
• 著者所属: Doshisha University
• 著者所属: National Institute of Information and Communications Technology
• 著者所属(英): en
• 著者所属(英): Doshisha University
• 著者所属(英): en
• 著者所属(英): Doshisha University
• 著者所属(英): en
• 著者所属(英): Doshisha University
• 著者所属(英): en
• 著者所属(英): Doshisha University
• 著者所属(英): en
• 著者所属(英): National Institute of Information and Communications Technology
• 著者名: Peng, Wang; Miho, Ohsaki; Kenji, Matsuda; Shigeru, Katagiri; Hideyuki, Watanabe
• 著者名(英): Peng, Wang; Miho, Ohsaki; Kenji, Matsuda; Shigeru, Katagiri; Hideyuki, Watanabe
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Imbalanced data classification is a common problem in applications related to the detection of anomalies, failures, and risks. Since previous problem-solving approaches were basically heuristic and task dependent, we propose a novel imbalanced data classifier with a theoretical problem-solving approach. Our proposed method fine-tunes the parameters of kernel logistic regression using the harmonic mean of such criteria as sensitivity and positive predictive value, which are derived based on a confusion matrix and are essential for multilateral evaluation. This paper presents the formulation of our proposed method and reports our empirical evaluation results.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Imbalanced data classification is a common problem in applications related to the detection of anomalies, failures, and risks. Since previous problem-solving approaches were basically heuristic and task dependent, we propose a novel imbalanced data classifier with a theoretical problem-solving approach. Our proposed method fine-tunes the parameters of kernel logistic regression using the harmonic mean of such criteria as sensitivity and positive predictive value, which are derived based on a confusion matrix and are essential for multilateral evaluation. This paper presents the formulation of our proposed method and reports our empirical evaluation results.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN10505667
• 書誌情報: 研究報告数理モデル化と問題解決（MPS）; 巻 2015-MPS-103, 号 55, p. 1-2, 発行日 2015-06-16
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8833
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会