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Journal of Healthcare Engineering
Volume 2018, Article ID 8902981, 9 pages
Research Article

Ensemble of Rotation Trees for Imbalanced Medical Datasets

1School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China
2Cooper Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou 450000, China
3Department of Neurology, Xinyang Central Hospital, Xinyang 464000, China
4School of Software Technology, Zhengzhou University, Zhengzhou 450001, China

Correspondence should be addressed to Huaping Guo; moc.361@mc_ougph and Wei She; nc.ude.uzz@ehsw

Received 22 August 2017; Revised 8 February 2018; Accepted 11 February 2018; Published 10 April 2018

Academic Editor: Maria Lindén

Copyright © 2018 Huaping Guo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Medical datasets are often predominately composed of “normal” examples with only a small percentage of “abnormal” ones and how to correctly recognize the abnormal examples is very meaningful. However, conventional classification learning methods try to pursue high accuracy by assuming that the number of any class examples is similar to each other, which lead to the fact that the abnormal class examples are usually ignored and misclassified to normal ones. In this paper, we propose a simple but effective ensemble method called ensemble of rotation trees (ERT) to handle this problem in imbalanced medical datasets. ERT learns an ensemble through the following four stages: (1) undersampling subsets from normal class, (2) obtaining new balanced training sets through combining each subset and abnormal class, (3) inducing a rotation matrix on randomly sampling subset of each new balanced set, and in each rotation matrix space, (4) learning a decision tree on each balanced training data. Here, the rotation matrix is mainly to improve the diversity between ensemble members, and undersampling technique aims to improve the performance of learned models on abnormal class. Experimental results show that, compared with other state-of-the-art methods, ERT shows significantly better performance for imbalanced medical datasets.