A Virtual Sample Generation Method Based on Kernel Density Estimation and Copula Function for Imbalanced Classification
Qun-Xiong Zhu, Shixiong Wang, Zhong-Sheng Chen, Yan‐Lin He, Yuan Xu · 2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS) · 2019
In the case of imbalanced data, classification models often achieve low accuracy. To solve this problem, this paper proposes a virtual sample generation method based on kernel density estimation and copula function. The kernel density estimation is used to estimate the probability density of each dimension of data, and the joint probability density of the samples is constructed by the copula function. The validation experiments are carried out by applying the proposed method to a numerical simulation and a yeast classification problem. Simulation results show that the proposed method can generate high-quality virtual samples and significantly improve the recognition accuracy.