Leveraging augmentation techniques for tasks with unbalancedness within the financial domain: a two-level ensemble approach

EPJ Data Science

Regular Article

Golshid Ranjbaran¹  Gianfranco Lombardo²  Diego Reforgiato Recupero³  Sergio Consoli⁴ 

[1] Department of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran;Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze, 43125, Parma, Italy;Department of Mathematics and Computer Science, University of Cagliari, via Ospedale 72, 09121, Cagliari, Italy;Joint Research Centre (DG JRC), European Commission, Via E. Fermi 2749, 21027, Ispra (VA), Italy;

关键词: Augmentation techniques;    Ensemble method;    Financial sector;    Machine learning;    Unbalanced data;   

DOI  :  10.1140/epjds/s13688-023-00402-9

 received in 2022-11-14, accepted in 2023-06-26,  发布年份 2023

来源: Springer

【 摘 要 】

Modern financial markets produce massive datasets that need to be analysed using new modelling techniques like those from (deep) Machine Learning and Artificial Intelligence. The common goal of these techniques is to forecast the behaviour of the market, which can be translated into various classification tasks, such as, for instance, predicting the likelihood of companies’ bankruptcy or in fraud detection systems. However, it is often the case that real-world financial data are unbalanced, meaning that the classes’ distribution is not equally represented in such datasets. This gives the main issue since any Machine Learning model is trained according to the majority class mainly, leading to inaccurate predictions. In this paper, we explore different data augmentation techniques to deal with very unbalanced financial data. We consider a number of publicly available datasets, then apply state-of-the-art augmentation strategies to them, and finally evaluate the results for several Machine Learning models trained on the sampled data. The performance of the various approaches is evaluated according to their accuracy, micro, and macro F1 score, and finally by analyzing the precision and recall over the minority class. We show that a consistent and accurate improvement is achieved when data augmentation is employed. The obtained classification results look promising and indicate the efficiency of augmentation strategies on financial tasks. On the basis of these results, we present an approach focused on classification tasks within the financial domain that takes a dataset as input, identifies what kind of augmentation technique to use, and then applies an ensemble of all the augmentation techniques of the identified type to the input dataset along with an ensemble of different methods to tackle the underlying classification.

【 授权许可】

CC BY   
© The Author(s) 2023

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