In many real world applications

the number of examples from different class is significantly different

which means the number of examples in major class is much larger than that of minor class.Therefore

learning from imbalanced data set has received much attention of machine learning and data mining community.Considering that most of previous research focus on binary class problem

this paper proposes a multi-class imbalance method based on HDDT ensemble.Empirical study shows that the method is effective for multi-class imbalance learning.