Applicability of Roughly Balanced Bagging for Complex Imbalanced Data
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چکیده
Roughly Balanced Bagging is based on under-sampling and classifies imbalanced data much better than other ensembles. In this paper, we experimentally study its properties that may influence its good performance. Results of experiments show that it can be constructed with a small number of component classifiers, which are quite accurate, however, of low diversity. Moreover, its good performance comes from its ability to recognize unsafe type of minority examples better than other ensembles. We also present how to improve its performance by integrating bootstrap sampling with random selection of attributes.
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تاریخ انتشار 2015