| Budget Amount *help |
¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2017: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2016: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Outline of Final Research Achievements |
In a wide range of domains such as cancer diagnosis, vehicle accident prediction, etc., there is a high demand for the classification of a small number of emergent instances (minority class) and a large number of ordinary instances (majority class). However, the imbalance of the two classes causes overlooking minorities. Conventional solutions for this were domain-specific and difficult to control the balance of performance between the classes. We therefore aim at the development of an imbalanced data classifier which is of high versatility and achieves the balance control and the improvement of performances. The proposed method is based on kernel logistic regression, minimum classification error and generalized probabilistic descent, and confusion matrix. The superiority of the proposed method to the conventional ones was confirmed by the evaluation experiments. We finally published an academic journal paper to report all this research results.
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