期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 29, 期 9, 页码 1806-1819出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2017.2682249
关键词
Imbalanced data; confusion matrix; kernel logistic regression; minimum classification error and generalized probabilistic descent
类别
资金
- JSPS KAKENHI [15K00323]
- MEXT-supported Program for the Strategic Research Foundation at Private Universities
- Grants-in-Aid for Scientific Research [15K00323] Funding Source: KAKEN
There have been many attempts to classify imbalanced data, since this classification is critical in a wide variety of applications related to the detection of anomalies, failures, and risks. Many conventional methods, which can be categorized into sampling, cost-sensitive, or ensemble, include heuristic and task dependent processes. In order to achieve a better classification performance by formulation without heuristics and task dependence, we propose confusion-matrix-based kernel logistic regression (CM-KLOGR). Its objective function is the harmonic mean of various evaluation criteria derived from a confusion matrix, such criteria as sensitivity, positive predictive value, and others for negatives. This objective function and its optimization are consistently formulated on the framework of KLOGR, based on minimum classification error and generalized probabilistic descent (MCE/GPD) learning. Due to the merits of the harmonic mean, KLOGR, and MCE/GPD, CM-KLOGR improves the multifaceted performances in a well-balanced way. This paper presents the formulation of CM-KLOGR and its effectiveness through experiments that comparatively evaluated CM-KLOGR using benchmark imbalanced datasets.
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