Jove
Visualize
Contact Us

Related Concept Videos

Survival Tree01:19

Survival Tree

201
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
201
Classification of Systems-II01:31

Classification of Systems-II

290
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
290
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

1.0K
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
1.0K
Force Classification01:22

Force Classification

1.9K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.9K
Classification of Systems-I01:26

Classification of Systems-I

380
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
380
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

6.2K
The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
6.2K

You might also read

Related Articles

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

Sort by
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
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 Experiment Video

Updated: Nov 3, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.0K

A Two-Stage Hybrid Default Discriminant Model Based on Deep Forest.

Gang Li1,2,3, Hong-Dong Ma1, Rong-Yue Liu1

  • 1School of Business Administration, Northeastern University, Shenyang 110819, China.

Entropy (Basel, Switzerland)
|June 2, 2021
PubMed
Summary

This study introduces a novel two-stage hybrid credit default prediction model using the gcForest algorithm. The model demonstrates superior performance in accuracy and robustness compared to existing methods, enhancing financial risk assessment.

Keywords:
credit loancredit scoredeep forestdefault discriminationfeature selection

Related Experiment Videos

Last Updated: Nov 3, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.0K

Area of Science:

  • Financial modeling
  • Machine learning applications
  • Credit risk assessment

Background:

  • Credit scoring models are vital for financial institutions to identify potential defaulters.
  • Current machine learning models, like deep learning, offer high accuracy but suffer from numerous hyperparameters and data dependency, limiting interpretability and robustness.
  • Significant improvements in interpretability and robustness are needed for credit scoring models.

Purpose of the Study:

  • To develop a two-stage hybrid credit default discrimination model.
  • To leverage the multi-grained Cascade Forest (gcForest) algorithm for enhanced default prediction.
  • To optimize the model for minimizing Type II error while maximizing AUC and accuracy.

Main Methods:

  • Constructed a two-stage hybrid model integrating multiple feature selection techniques with the gcForest algorithm.
  • Employed gcForest, a deep forest model based on random forest, known for its efficiency with high-dimensional data and fewer hyperparameters.
  • Optimized model parameters prioritizing Type II error reduction, followed by AUC and accuracy maximization.

Main Results:

  • Validated the hybrid model's effectiveness using Australian, Japanese, and German credit datasets from the UCI database.
  • Demonstrated superior performance of gcForest over single classifiers (ANN) and ensemble classifiers (LightGBM, CNNs) in Type II error, AUC, and accuracy.
  • Confirmed the model's robustness and effectiveness through comparative analysis with existing research.

Conclusions:

  • The proposed gcForest-based hybrid model outperforms popular classifiers in critical credit default prediction metrics.
  • The model effectively balances interpretability and robustness with high predictive accuracy.
  • This approach offers a significant advancement in credit risk assessment tools for financial institutions.