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| EasyEnsemble.M for Multiclass Imbalance Problem |
| LI Qian-Qian1, LIU Xu-Ying1,2 |
1.Key Laboratory of Computer Network and Information Integration, Ministry of Education, School of Computer Science and Engineering, Southeast University, Nanjing 211189
2.National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 211189 |
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Abstract The potential useful information in the majority class is ignored by stochastic under-sampling. When under-sampling is applied to multi-class imbalance problem, this situation becomes even worse. In this paper, EasyEnsemble.M for multi-class imbalance problem is proposed. The potential useful information contained in the majority classes which is ignored is explored by stochastic sampling the majority classes for multiple times. Then, sub-classifiers are learned and a strong classifier is obtained by using hybrid ensemble techniques. Experimental results show that EasyEnsemble.M is superior to other frequently used multi-class imbalance learning methods when G-mean is used as performance measure.
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Received: 13 May 2013
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多类类别不平衡问题的困难在于类别间样本分布的多样化和概念复杂度的增加,各类别样本数目只占总训练集的一小部分.即使在类别平衡的情况下,该类样本也很难成功的学习到正确概念.
随机欠采样方法广泛应用于两类类别不平衡分类问题中,优点是简单高效,缺点忽略了很多潜在有用的大类样本信息.有研究指出[7],与随机过采样方法相比,随机欠采样方法与之相当甚至更优.在面对多类的类别不平衡问题时,尤其是当小类样本数非常少的情况,欠采样方法忽略潜在有用信息的缺点会带来更严重的问题.直接使用欠采样方法很难得到很好的分类性能[10].
[17] Sun Y, Kamel M S, Wang Y. Boosting for Learning Multiple Classes with Imbalanced Class Distribution [C]. In: Proceedings of the 6th IEEE Industrial Conference on Data Ming, 2006: 592-602. |
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2014, Vol. 27 
