摘要：

欠抽样方法在非平衡数据集分类时,未充分考虑数据分布变化对分类结果造成的影响。为此,提出一种基于聚类融合去冗余的改进欠抽样方法。采用聚类算法得到多数类样本高密度分布区域的聚类中心,将多数类样本划分为不同子集,通过计算各子集的相似度冗余系数对多数类样本进行去冗余删除,以达到欠抽样的目的。对15个不同平衡率的数据集欠抽样后,利用代价敏感混合属性多决策树模型进行分类。实验结果表明,在不降低非平衡数据集分类准确率的前提下，该方法能够提高少数类样本的正类率及预测模型的G-mean值。

关键词: 非平衡数据集, 聚类算法, 欠抽样, 去冗余, 多决策树预测模型

Abstract:

The removal under-sampling method does not consider much the influence of data distribution changes on the classification results when the unbalanced data sets are classified,an improved under-sampling method based on clustering fusion and redundancy removal is proposed.The clustering algorithm is used to obtain the clustering centers of the high-density distribution regions of most samples.Most of the samples are divided into different subsets.The redundancy coefficients of each subset are calculated to de-redundantly delete most of the samples.After under-sampling the data sets with different balance rates,the cost-sensitive attribute hybrid strategy multi-decision tree prediction model is used for classification.Experimental results show that the proposed method can enable the prediction model to improve the positive rate of a few samples and the G-mean value of the prediction model under the premise of ensuring that the classification accuracy of the unbalanced data sets is not reduced.

Key words: imbalanced data sets, clustering algorithm, under-sampling, redundancy removal, multi-decision tree prediction model

中图分类号: 

• TP391.4