Safe level graph for synthetic minority over-sampling techniques
In the class imbalance problem, most existent classifiers which are designed by the distribution of balance datasets fail to recognize minority classes since a large number of negative instances can dominate a few positive instances. Borderline-SMOTE and Safe-Level-SMOTE are over-sampling techniques...
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| Main Authors: | , |
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| 格式: | Conference Proceeding |
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2018
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| 在線閱讀: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84891076473&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/52410 |
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| 機構: | Chiang Mai University |
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