关键词: 不平衡数据集, 集成学习, AdaBoost, 重采样

Abstract: A new training method of AdaBoost（ILAdaboost） which is good for unbalanced datasets is proposed in this paper.The algorithm evaluates the original data with the base classifier of each iteration.It divides the original dataset into four subsets，and then re-samples in the four subsets to form the balanced datasets，using for the base classifier learning in the next iteration.Due to the inclination to the minority and the false classified majority in the process of re-sampling，the decision surface in using synthetic classifier deviates from the minority.Based on the experiment of the 10 classical unbalanced datasets from UCI，the algorithm greatly increases the accuracy of minority and the GMA，keeping the accuracy of majority.

Key words: unbalanced dataset, ensemble learning, AdaBoost, re-sample