Abstract: In case of machine learning, the problem of class imbalance is always troubling, i. e. one class of the samples has a larger magnitude than the other classes. This problem brings a preference of the classifier to the majority class, which leads to worse performance of the classifier on the minority class. We proposed an improved boosting tree (BT) algorithm for learning imbalanced data, called cost BT. In each iteration of the cost BT, only the weights of the misclassified minority class samples are increased. Meanwhile, the error rate in the weight formula of the base classifier is replaced by 1 minus F-measure. In this study, the performance of the cost BT algorithm is compared with other known methods on 9 public data sets. The compared methods include the decision tree and
random forest algorithm, and both of them were combined with the sampling techniques such as synthetic minority oversampling technique (SMOTE), Borderline-SMOTE, adaptive synthetic sampling approach (ADASYN) and one sided selection. The cost BT algorithm performed better than the other compared methods in F-measure, G-mean and area under curve (AUC). In 6 of the 9 data sets, the cost BT algorithm has a superior performance to the other published methods. It can promote the prediction performance of the base classifiers by increasing the proportion of the minority class in the whole samples with only increasing the weights of the misclassified minority class samples in each iteration of the BT. In addition, computing the weights of the base classifiers with F-measure is helpful to the ensemble decisions.

Key words: machine learning, class imbalanced, BT, data sampling