Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
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AdaBoost algorithm is proved to be a very efficient classification method for the balanced dataset with all classes having similar proportions. However, in real application, it is quite common to have unbalanced dataset with a certain class of interest having very small size. It will be problematic since the algorithm might predict all the cases into majority classes without loss of overall accuracy. This paper proposes an improved AdaBoost algorithm called BABoost (Balanced AdaBoost), which gives higher weights to the misclassified examples from the minority class. Empirical results show that the new method decreases the prediction error of minority class significantly with increasing the prediction error of majority class a little bit. It can also produce higher values of margin which indicates a better classification method.