关键词: 二次学习, 集成学习, 类别不平衡学习, 数据挖掘

Abstract: This CCDM 2014 Data Mining Competition focused on the medical diagnosis datasets. It proposed two popular problems in real world, i.e., multi-label problem and multi-class classification problem. In order to solve these data mining tasks much better, aiming at the problems of class imbalance and less training instances, this paper proposes a new learning framework, i.e., the twice ensemble learning framework, to tackle these difficulties. This new framework can get some instances with higher confidence, and inputs them into the original training dataset. Finally, it can obtain classifiers with higher generalization capability. The results of this competition show that the twice ensemble learning framework can get very ideal performance on these two problems.

Key words: twice learning, ensemble learning, class imbalance learning, data mining