| Summary: | 碩士 === 國立交通大學 === 統計學研究所 === 104 === The problem of learning from imbalanced data has been receiving a growing attention. Since dealing with imbalanced data may decrease the efficiency of classifier, many researchers have been working on this domain and coming up with many solutions, such as the method of combining SMOTE(Synthetic Minority Over-sampling Technique) and decision tree. In this study, we review the existing methods including SMOTE, Borderline SMOTE, Adaptive Boosting and SMOTE Boosting. To improve these methods, we propose an approach Borderline SMOTE Boosting. This approach is compared with the existing methods using three real data examples. The results show that the proposed method leads to a better result.
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