# A comparison of Deep Learning performances with other machine learning algorithms on credit scoring unbalanced data

arXiv: https://arxiv.org/abs/1907.12363 · HTML: https://mlsanity.com/paper/1907.12363/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2019.

## Built on by (most cited first, 3 of 3)

- [Machine learning on small size samples: A synthetic knowledge synthesis](https://mlsanity.com/paper/2103.01002/) (2021)
- [Learning Transactions Representations for Information Management in Banks: Mastering Local, Global, and External Knowledge](https://mlsanity.com/paper/2404.02047/) (2024)
- [Performance and Interpretability Comparisons of Supervised Machine Learning Algorithms: An Empirical Study](https://mlsanity.com/paper/2204.12868/) (2022)

## Abstract (arXiv)

Training models on highly unbalanced data is admitted to be a challenging task for machine learning algorithms. Current studies on deep learning mainly focus on data sets with balanced class labels or unbalanced data, but with massive amount of samples available, like in speech recognition. However, the capacities of deep learning on imbalanced data with little samples is not deeply investigated in literature, while it is a very common application context in numerous industries. To contribute to fill this gap, this paper compares the performances of several popular machine learning algorithms previously applied with success to unbalanced data set with deep learning algorithms. We conduct those experiments on a highly unbalanced data set, used for credit scoring. We evaluate various configuration including neural network optimization techniques and try to determine their capacities when they operate with imbalanced corpora.