现实中数据分布不平衡的情况越来越多，给以数据分布基本均衡为前提的传统分类算法带来了一定的挑战。利用相关的辅助数据进行迁移学习可以解决目标数据的分布不平衡问题。本文以TrAdaboost算法为基础，提出了一个针对不平衡数据的二分类迁移学习算法UnbalancedTrAdaboost（UBTA）。UBTA算法利用不同类别的Precision-Recall曲线下的面积auprc（the Area Under the Precision-Recall Curve）计算弱分类器权重，对不同类别的样本采取不同的权重更新策略。由于AUC指标对数据分布变化不敏感，结合G-mean和BER能更准确地评估不平衡分类算法的性能。综合三种指标的实验结果表明，UBTA具有较好的分类性能，既能提升对少数类的关注，又能保持多数类的分类准确度。

Traditional classification algorithms based on the balance data meet some challenges, when data distribution become more and more imbalanced. Transfer learning can solve the problem of imbalanced data distribution by using the relevant auxiliary data sets to compensate the imbalanced target data set. In this paper, we proposed the UnbalancedTrAdaboost(UBTA) binary classification algorithm based on TrAdaboost, which calculates the weights of weak classifiers usingthe auprc (the Area Under the Precision-Recall Curve) of different classes and updates the weights of misclassified data of different classes with different mechanisms. The AUC measure is more accurate combined with G-mean and BER when evaluated the unbalanced classification, since AUC is insensitive to changes in class distribution. The results of these three metrics indicate that, the UBTA algorithm achieves better performance for imbalanced data and classifies more minority instances with the high accuracy of majority instances.