Under-sampling Method Based on One-pass Clustering for Imbalanced Data Distribution
Sampling is a widely used method in dealing imbalanced dataset.The main idea of sampling is changing the distribution of various classes in a dataset,decreasing the difference in the distribution between majority class and minority class.This paper proposes a under-sampling approaches based on one-pass clustering for imbalance problem.According to the features of each clusters and the degree of data inclination,a sample Ratio is made for each clusters.Hence each cluster can be sampled with its sample ratio.A Cluster with greater density will be sampled into comparatively less selection.In contrast,a Cluster with smaller density will be more or entirely sampled.With decreasing the amount of the whole dataset,the amount of minority class can be ensured.The experimental result show that our sample approaches can make the sample form the imbalanced data more typical and improve the performance in classification and clustering.