Exploratory Under-Sampling for Class-Imbalance Learning

Under-sampling is a class-imbalance learning method which uses only a subset of major class examples and thus is very efficient. The main deficiency is that many major class examples are ignored. We propose two algorithms to overcome the deficiency. EasyEnsemble samples several subsets from the major class, trains a learner using each of them, and combines the outputs of those learners. BalanceCascade is similar to EasyEnsemble except that it removes correctly classified major class examples of trained learners from further consideration. Experiments show that both of the proposed algorithms have better AUC scores than many existing class-imbalance learning methods. Moreover, they have approximately the same training time as that of under-sampling, which trains significantly faster than other methods.

• Type: Conference
• Year: 2006
• Where: ICDM
• Authors: Xu-Ying Liu, Jianxin Wu, Zhi-Hua Zhou
• Keywords: Better Auc Scores, Class-imbalance Learning Methods, Data Mining, ICDM 2006, Major Class Examples