【摘要】 随机欠采样方法忽略潜在有用的大类样本信息,在面对多类分类问题时更为突出.文中提出多类类别不平衡学习算法:EasyEnsemble.M.该算法通过多次针对大类样本随机采样,充分利用被随机欠采样方法忽略的潜在有用的大类样本,学习多个子分类器,利用混合的集成技术最终得到性能较优的强分类器.实验结果表明,与常用的多类类别不平衡学习算法相比,EasyEnsemble.M可有效提高分类器的G-mean值.更多还原

【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.更多还原