DeepAI AI Chat
Log In Sign Up

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

07/25/2019
by   Louis Marceau, et al.
BANQUE NATIONALE DU CANADA
0

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 an highly unbalanced data set, used for credit scoring. We evaluate various configuration including neural network optimisation techniques and try to determine their capacities when they operate with imbalanced corpora.

READ FULL TEXT

page 1

page 2

page 3

page 4

02/09/2021

Classification of Imbalanced Credit scoring data sets Based on Ensemble Method with the Weighted-Hybrid-Sampling

In the era of big data, the utilization of credit-scoring models to dete...
10/26/2020

Balanced cooperative modeling

Machine learning techniques are often used for supporting a knowledge en...
05/21/2022

Deep Learning vs. Gradient Boosting: Benchmarking state-of-the-art machine learning algorithms for credit scoring

Artificial intelligence (AI) and machine learning (ML) have become vital...
06/10/2021

A Mathematical Foundation for Robust Machine Learning based on Bias-Variance Trade-off

A common assumption in machine learning is that samples are independentl...
06/30/2022

Discrimination in machine learning algorithms

Machine learning algorithms are routinely used for business decisions th...
03/06/2020

CNN-based Repetitive self-revised learning for photos' aesthetics imbalanced classification

Aesthetic assessment is subjective, and the distribution of the aestheti...