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A Cluster-based Regrouping Approach for Imbalanced Data Distributions

机译:基于集群的不平衡数据分配重组方法

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In real-world applications,it has been observed that class imbalance (significant differences in class prior probabilities) may produce an important deterioration of the classifier performance, in particular with patterns belonging to the less represented classes. In this paper, we propose a Clusterbased Regrouping approach (CR) which divides the whole training data into positive group and negative group by clustering through the outlier factor. As a result, the similar samples will be in the same group while the dissimilar samples will be in the different groups. Then the basic classifier is employed to build the models on both the positive group and the negative group respectively. When classifying the new object, the model used to evaluate will be chosen according to the type of the group which the new object is nearest. The experimental results demonstrate that our approach achieved promising performance in some cases by directly or indirectly reducing the class distribution skewness.
机译:在实际应用中,已经观察到类别不平衡(类别先验概率的显着差异)可能会严重影响分类器的性能,特别是对于属于较少表示类别的模式而言。在本文中,我们提出了一种基于聚类的重组方法(CR),该方法通过通过离群因素进行聚类将整个训练数据分为阳性组和阴性组。结果,相似的样本将在同一组中,而不同的样本将在不同的组中。然后使用基本分类器分别在正组和负组上建立模型。在对新对象进行分类时,将根据新对象最接近的组的类型来选择用于评估的模型。实验结果表明,在某些情况下,我们的方法通过直接或间接减少类分布偏斜度而获得了令人满意的性能。

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