@inproceedings{7dd5fab2f2024e31a89666c7daa9d547,
title = "A virtual sample generation method based on kernel density estimation and copula function for imbalanced classification",
abstract = "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.",
keywords = "Classification, Copula, Imbalance dataset, Kernel density estimation, Virtual sample generation",
author = "Qunxiong Zhu and Shixiong Wang and Zhongsheng Chen and Yanlin He and Yuan Xu",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 8th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2019 ; Conference date: 24-05-2019 Through 27-05-2019",
year = "2019",
month = may,
doi = "10.1109/DDCLS.2019.8908870",
language = "English",
series = "Proceedings of 2019 IEEE 8th Data Driven Control and Learning Systems Conference, DDCLS 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "969--975",
booktitle = "Proceedings of 2019 IEEE 8th Data Driven Control and Learning Systems Conference, DDCLS 2019",
address = "United States",
}