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Learning from imbalanced datasets is a well known problem in the data mining community. Many techniques have been proposed to alleviate the problems associated with class imbalance, including data sampling and boosting. While data sampling has received the bulk of the attention from the research community, our results show that boosting often results in better classification performance than even...
This paper discusses a comprehensive suite of experiments that analyze the performance of the random forest (RF) learner implemented in Weka. RF is a relatively new learner, and to the best of our knowledge, only preliminary experimentation on the construction of random forest classifiers in the context of imbalanced data has been reported in previous work. Therefore, the contribution of this study...
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