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

Yang Huang, Duen Ren Liu, Shin Jye Lee*, Chia Hao Hsu, Yang Guang Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)90-105
Number of pages16
JournalInformation sciences
Volume590
DOIs
StatePublished - Apr 2022

Keywords

  • Boosting resampling
  • Conditional variational autoencoder
  • Regression problem

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