OAGAN: AN OVERSAMPLING APPROACH FOR IMBALANCED DATA PROBLEMS

Jiali Cheng, Bofei Wei, Feng Liu, Sijie Han, Zhiqiang Cai, Shubin Si

Research output: Contribution to journalConference articlepeer-review

Abstract

Imbalanced data problems are prevalent in our daily lives. When conducted on this kind of data, many classification models may encounter many difficulties and produce ineffective performance due to a high training bias towards the majority of class instances. In order to solve the imbalanced data problem, many effective oversampling methods have been proposed. However, few methods in previous work consider different weights for minority class samples. Therefore, we proposed an oversampling method for the minority class by Adaptive Synthetic Sampling with Conditional Tabular Generative Adversarial Network (OAGAN) in this study. Firstly, Adaptive Synthetic Sampling is used to assign different weights to each minority class sample, based on which new similar samples are generated by the Conditional Tabular Generative Adversarial Network. In other words, the weights of different minority class samples are considered when generating data using the Conditional Tabular Generative Adversarial Network, thus improving the method. The proposed OAGAN method was evaluated on ten imbalanced datasets and three standard oversampling algorithms, which achieved better learning performance.

Original languageEnglish
Pages (from-to)734-739
Number of pages6
JournalIET Conference Proceedings
Volume2023
Issue number9
DOIs
StatePublished - 2023
Event13th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2023 - Kunming, China
Duration: 26 Jul 202329 Jul 2023

Keywords

  • DIFFERENT WEIGHTS
  • IMBALANCED DATA
  • OAGAN ALGORITHM

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