基于K-means聚类和遗传算法的少数类样本采样方法研究

传统的分类器对不均衡数据集的分类严重倾向于多数类.为了有效地提高不均衡数据集中少数类的分类性能,针对此问题提出了一种基于K-means聚类和遗传算法的少数类样本采样方法.通过K-means算法将少数类样本聚类分组,在每个聚类内使用遗传算法获取新样本并进行有效性验证,最后通过使用KNN和SVM分类器,在仿真实验中证明了方法的有效性.

The research of minority kind of sample sampling method based on K-means cluster and genetic algorithm

The classification favors seriously to the most kinds when we use the traditional sorter to classify the imbalanced data set. In order to effectively enhance classified performance of the minority kind in the imbalanced data set, we proposed one kind minority kind of sample sampling method based on the K-means cluster and the genetic algorithm in view of this question. We used K-means algorithm to cluster and group the minority kind of sample, and in each cluster we use the genetic algorithm to gain the new sample and to carry on the valid confirmation. Finally, through using KNN and SVM sorter we proved the method validity in the simulation experiment.