http://lamda.nju.edu.cn/code_EasyEnsemble.ashx
http://cse.seu.edu.cn/people/xyliu/publication/tsmcb09.pdf
其实处理类别不均衡问题主要还是依靠 采样，这里做些ensemble 无外乎就两种 random 然后是 adboost这里也是如此！
Under-sampling is a popular method in dealing with class-imbalance problems, which uses only a subset of the majority class and thus is very efficient. The main deficiency is that many majority class examples are ignored. We propose two algorithms to overcome this deficiency. EasyEnsemble samples several subsets from the majority class, trains a learner using each of them, and combines the outputs of those learners. BalanceCascade trains the learners sequentially, where in each step the majority class examples which are correctly classified by the current trained learners are removed from further consideration.