Survey on highly imbalanced multi-class data

• Main Authors: Abdul Hamid, Mohd Hakim, Yusoff, Marina, Mohamed, Azlinah
• Format: Article
• Language: English
• Published: The Science and Information Organization 2022
• Online Access: http://eprints.utem.edu.my/id/eprint/26188/2/PAPER_27-SURVEY_ON_HIGHLY_IMBALANCED_MULTI_CLASS_DATA.PDF; http://eprints.utem.edu.my/id/eprint/26188/; https://thesai.org/Downloads/Volume13No6/Paper_27-Survey_on_Highly_Imbalanced_Multi_class_Data.pdf
• Institution: Universiti Teknikal Malaysia Melaka

Machine learning technology has a massive impact on society because it offers solutions to solve many complicated problems like classification, clustering analysis, and predictions, especially during the COVID-19 pandemic. Data distribution in machine learning has been an essential aspect in providing unbiased solutions. From the earliest literatures published on highly imbalanced data until recently, machine learning research has focused mostly on binary classification data problems. Research on highly imbalanced multi-class data is still greatly unexplored when the need for better analysis and predictions in handling Big Data is required. This study focuses on reviews related to the models or techniques in handling highly imbalanced multi-class data, along with their strengths and weaknesses and related domains. Furthermore, the paper uses the statistical method to explore a case study with a severely imbalanced dataset. This article aims to (1) understand the trend of highly imbalanced multi-class data through analysis of related literatures; (2) analyze the previous and current methods of handling highly imbalanced multi-class data; (3) construct a framework of highly imbalanced multi-class data. The chosen highly imbalanced multi-class dataset analysis will also be performed and adapted to the current methods or techniques in machine learning, followed by discussions on open challenges and the future direction of highly imbalanced multi-class data. Finally, for highly imbalanced multi-class data, this paper presents a novel framework. We hope this research can provide insights on the potential development of better methods or techniques to handle and manipulate highly imbalanced multi-class data.