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...
محفوظ في:
| المؤلفون الرئيسيون: | , |
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| التنسيق: | وقائع المؤتمر |
| منشور في: |
2018
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84891076473&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/52410 |
| الوسوم: |
إضافة وسم
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| المؤسسة: | Chiang Mai University |
| الملخص: | 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 which are applied to handle this situation by generating synthetic instances in different regions. The former operates on the border of a minority class while the latter works inside the class far from the border. Unfortunately, a data miner is unable to conveniently justify a suitable SMOTE for each dataset. In this paper, a safe level graph is proposed as a guideline tool for selecting an appropriate SMOTE and describes the characteristic of a minority class in an imbalance dataset. Relying on advice of a safe level graph, the experimental success rate is shown to reach 73% when an F-measure is used as the performance measure and 78% for satisfactory AUCs. © 2013 IEEE. |
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