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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...
Learning with class imbalanced data sets is a challenging undertaking by the common learning algorithms. These algorithms favor majority class due to imbalanced class representation, noise and their inability to expand the boundaries of minority class in concept space. To improve the performance of minority class identification, ensembles combined with data resampling techniques have gained much popularity...
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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