A boosting resampling method for regression based on a conditional variational autoencoder

• Published in: Information sciences Vol. 590; pp. 90 - 105
• Main Authors: Huang, Yang, Liu, Duen-Ren, Lee, Shin-Jye, Hsu, Chia-Hao, Liu, Yang-Guang
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.04.2022
• Subjects: Boosting resampling; Conditional variational autoencoder; Regression problem; Conditional variational autoencoder; Regression problem; Boosting resampling
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2021.12.100

•Proposes a novel resampling method based on a deep generative model to deal with the imbalanced regression data sets.•Improves the effect of undersampling and reduces the number of normal samples without losing informative samples by the proposed undersampling method.•Improves the quality of new rare regression samples, can better capture and learn information from rare samples by introducing the boosting mechanism and deep generative model. Resampling is the most commonly used method for dealing with imbalanced data, in addition to modifying the algorithm mechanism, it can, for example, generate new minority samples or reduce majority samples to adjust the data distribution. However, to date, related research has predominantly focused on solving the classification problem, while the issue of imbalanced regression data has rarely been discussed. In real-world applications, predicting regression data is a common and valuable issue in decision making, especially in regard to those rare samples with extremely high or low values, such as those encountered in the fields of signal processing, finance, or meteorology. This study therefore divided its regression data into rare samples and normal samples, with self-defined relevance functions and, in addition, proposed a boosting resampling method based on a conditional variational autoencoder. The experimental results showed that when using the proposed resampling method was employed, the prediction performance of the whole testing data set was slightly increased, while the performance for the rare samples was significantly improved.