Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

12.3K
When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
12.3K
Actuarial Approach01:20

Actuarial Approach

140
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
140
Regression Analysis01:11

Regression Analysis

6.1K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
6.1K
Aggregates Classification01:29

Aggregates Classification

389
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
389
Multiple Regression01:25

Multiple Regression

3.2K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.2K
Correlation and Regression00:53

Correlation and Regression

2.0K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
2.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Perioperative anesthetic management for resection of giant retroperitoneal tumors.

Frontiers in oncology·2026
Same author

Strain-Based Monitoring Methodology and Numerical Validation for the Evaluation of Transverse Connection Condition in Precast Multi-Girder Bridges.

Sensors (Basel, Switzerland)·2026
Same author

Correction: Camel Whey protein ameliorates type Ⅰ diabetic cardiomyopathy by mitigating oxidative stress, inflammation and apoptosis.

Food science of animal resources·2026
Same author

Both internal and external switches jointly determine seed dormancy and germination.

Plant science : an international journal of experimental plant biology·2026
Same author

Chemical methodologies for Direct-to-Biology library synthesis.

European journal of medicinal chemistry·2026
Same author

TRAIP promotes the development of papillary thyroid cancer by inhibiting TRAF2-mediated BRAF ubiquitination.

The Journal of biological chemistry·2026

Related Experiment Video

Updated: Sep 17, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.5K

SMOTE algorithm optimization and application in corporate credit risk prediction with diversification strategy

Han Wei1

  • 1Faculty of Economics and Management, Xi'an Kedagaoxin University, Xi'an, 710000, Shaanxi, China. weih53476@outlook.com.

Scientific Reports
|July 2, 2025
PubMed
Summary

This study optimizes the Synthetic Minority Over-Sampling Technique (SMOTE) for corporate credit risk prediction, improving accuracy by over 21% and enhancing financial risk management for diversified companies.

Keywords:
Corporate diversificationCredit riskFinancial sectorRisk monitoringSMOTE algorithm

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

751
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.8K

Related Experiment Videos

Last Updated: Sep 17, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.5K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

751
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.8K

Area of Science:

  • Financial Risk Management
  • Machine Learning in Finance
  • Corporate Finance

Background:

  • Corporate diversification complicates credit risk prediction due to heterogeneous financial structures and performance across business units.
  • Financial institutions require advanced monitoring and evaluation for cross-sectoral risks in diversified enterprises.
  • Existing credit risk models face challenges with imbalanced datasets common in corporate finance.

Purpose of the Study:

  • To optimize the Synthetic Minority Over-Sampling Technique (SMOTE) algorithm for enhanced corporate credit risk prediction.
  • To analyze the impact of corporate diversification strategies on credit risk assessment.
  • To provide financial institutions with improved algorithmic tools for managing risks in complex corporate environments.

Main Methods:

  • Developed an optimized SMOTE algorithm incorporating an adaptive boundary adjustment mechanism and an optimized weight allocation protocol.
  • Systematically analyzed corporate diversification strategies and their financial implications.
  • Validated the optimized SMOTE algorithm using four benchmark datasets (German Credit, Australian Credit Approval, Taiwan Credit Card Default, Corporate Credit Risk Assessment).

Main Results:

  • The optimized SMOTE algorithm significantly outperformed six comparison models in credit risk prediction.
  • Achieved accuracy improvements exceeding 21% (up to 38%), precision over 28% (up to 35%), recall over 31% (up to 42%), and F1-score approximately 33% (up to 39%).
  • Demonstrated the effectiveness of adaptive boundary adjustment and optimized weight allocation in generating more representative synthetic samples.

Conclusions:

  • The optimized SMOTE algorithm offers a superior solution for credit risk prediction in diversified corporate settings.
  • Enhanced prediction accuracy strengthens financial institutions' risk management capabilities and decision-making.
  • The study contributes to financial market stability by improving credit risk assessment accuracy.