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A data set is considered imbalanced when its class representation is substantially different. Examples of rare class are infrequent and cost more than common class examples in binary class imbalance data sets. Common learners usually incline toward common class and rare class examples are missed due to class imbalance. Ensemble learning approach combined with data resampling gains popularity to solve...
MultiBoost ensemble has been well acknowledged as an effective learning algorithm which able to reduce both bias and variance in error and has high generalization performance. However, to deal with the class imbalanced learning, the Multi- Boost shall be amended. In this paper, a new hybrid machine learning method called Distribution based MultiBoost (DBMB) for class imbalanced problems is proposed,...
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