滚动轴承缺陷是导致滚动轴承在运行过程中产生故障的主要原因之一，因此对滚动轴承缺陷诊断技术进行研究具有十分重要的意义。但是，在轴承故障诊断数据集中，故障样本数通常比非故障样本数要少很多，由此引发了数据不均衡情况下故障诊断的问题。以往的研究很少关注这种数据不均衡问题对故障诊断的影响。此外，在故障数据集中有一些冗余甚至是不相关的特征，这些特征降低了学习器的泛化能力。为解决这类问题，本文提出了一种基于Fisher准则的EasyEnsemble算法来解决故障诊断中的数据不均衡问题。在UCI数据集和滚动轴承数据集上的实验结果表明，新算法提高了分类器在不均衡数据集上的分类性能和预报能力。

Defect is one of the important factors resulting in the bearing fault, so it is significant to study the technology of defect diagnosis for rolling bearing. The class imbalance problem is encountered in the fault diagnosis, which causes seriously negative effect on the performance of classifiers that assume a balanced distribution of classes. Though it is critical, few previous works paid attention to this class imbalance problem in the fault diagnosis of bearing. In the imbalanced data problems, some features are redundant and even irrelevant. These features will hurt the generalization performance of learning machines. Here we propose FEE (Fisher criterion feature selection for EasyEnsemble) to solve the class imbalanced problem in the fault diagnosis of bearing. The experimental results on the UCI data set and the bearing data set show that FEE improves the classification performance and prediction ability on the imbalanced dataset.