A new oversampling method and improved radial basis function classifier for customer consumption behavior prediction

作者：

Highlights：

• A new oversampling method is proposed to solve class imbalance in the real dataset.

• A new classifier is proposed to improve the accuracy of consumer behavior prediction.

• The hybrid kernel of the radial basis function neural network is designed.

• The centers of the neural network are optimized by the immune algorithm.

• All results show that the proposed two methods outperform existing models.

摘要

•A new oversampling method is proposed to solve class imbalance in the real dataset.•A new classifier is proposed to improve the accuracy of consumer behavior prediction.•The hybrid kernel of the radial basis function neural network is designed.•The centers of the neural network are optimized by the immune algorithm.•All results show that the proposed two methods outperform existing models.

论文关键词：Imbalanced data,Oversampling technique,RBF neural network,Immune algorithm,Consumer consumption behavior prediction

论文评审过程：Received 19 March 2021, Revised 9 March 2022, Accepted 23 March 2022, Available online 26 March 2022, Version of Record 29 March 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.116982