logo
ResearchBunny Logo
Credit risk assessment using the factorization machine model with feature interactions

Business

Credit risk assessment using the factorization machine model with feature interactions

J. Quan and X. Sun

This research, conducted by Jing Quan and Xuelian Sun, demonstrates the effectiveness of the factorization machine model in credit risk assessment. With impressive accuracy and computational efficiency, this study positions FM as a leading candidate in the field, outperforming traditional models like logistic regression and support vector machine.

00:00
00:00
Playback language: English
Abstract
This article applies the factorization machine (FM) model to credit risk assessment. One-hot encoding addresses non-numerical features, resulting in sparse data. The study compares FM's performance against logistic regression, support vector machine, k-nearest neighbors, and artificial neural network on four real-world datasets. Results show FM achieves higher accuracy and computational efficiency, making it a suitable candidate for credit risk assessment.
Publisher
Humanities and Social Sciences Communications
Published On
Feb 08, 2024
Authors
Jing Quan, Xuelian Sun
Tags
credit risk assessment
factorization machine
machine learning
data analysis
real-world datasets

Related Publications

Explore these studies to deepen your understanding of the subject.

Developing a BOPPPS (Bridge-in, Objectives, Pre-assessment, Participatory Learning, Post-assessment and Summary) model combined with the OBE (Outcome Based Education) concept to improve the teaching outcomes of higher education
Education

Developing a BOPPPS (Bridge-in, Objectives, Pre-assessment, Participatory Learning, Post-assessment and Summary) model combined with the OBE (Outcome Based Education) concept to improve the teaching outcomes of higher education

Z. Xu, L. Ge, et al.

Machine-learning algorithms for asthma, COPD, and lung cancer risk assessment using circulating microbial extracellular vesicle data and their application to assess dietary effects
Medicine and Health

Machine-learning algorithms for asthma, COPD, and lung cancer risk assessment using circulating microbial extracellular vesicle data and their application to assess dietary effects

A. Mcdowell, J. Kang, et al.

Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection
Medicine and Health

Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection

Y. Takahashi, M. Ueki, et al.

Scale-invariant machine-learning model accelerates the discovery of quaternary chalcogenides with ultralow lattice thermal conductivity
Engineering and Technology

Scale-invariant machine-learning model accelerates the discovery of quaternary chalcogenides with ultralow lattice thermal conductivity

K. Pal, C. W. Park, et al.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny