Solving Multi-class Imbalance Problems Using Improved Tabular GANs

Farou, Zakarya; Kopeikina, Liudmila; Horváth, Tomáš

Abstract

Multi-class imbalance problems are non-standard derivative data science problems. These problems are associated with the skewness in the data underlying distribution, which, in turn, raises numerous issues for conventional machine learning techniques. To address the lack of data in imbalance problems, we can either collect new data or oversample the underrepresented classes by synthesizing artificial data from original instances. This paper focuses on the latter and introduces two novel tabular GAN variants to handle multi-class imbalance problems. Empirical results on three datasets from the UCI repository demonstrated that the suggested approaches that use our proposed filtering algorithm based on neighboring rules improved the ability of the decision tree classification model to recognize underrepresented class instances, decreased the bias toward the majority class, and enhanced its generalization ability.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: 23rd International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)
• Start Date: Nov 24, 2022
• End Date: Nov 26, 2022
• Online Publication Date: Nov 21, 2022
• Publication Date: 2022
• Deposit Date: Apr 8, 2024
• Publisher: Springer
• Pages: 527-539
• Series Title: Lecture Notes in Computer Science
• Series Number: 13756
• Series ISSN: 0302-9743
• Book Title: Intelligent Data Engineering and Automated Learning – IDEAL 2022: 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings
• ISBN: 9783031217524
• DOI: https://doi.org/10.1007/978-3-031-21753-1_51
• Keywords: Imbalanced learning, Generative adversarial networks, Data augmentation, Data filtering, Multi-class classification
• Public URL: http://researchrepository.napier.ac.uk/Output/3587387
• Related Public URLs: https://ideal-conf.com/ideal2022