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Related Experiment Video

Updated: Nov 4, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Internet of Things Enabled Financial Crisis Prediction in Enterprises Using Optimal Feature Subset Selection-Based

Noura Metawa¹, Phong Thanh Nguyen², Quyen Le Hoang Thuy To Nguyen³

¹Faculty of Commerce, Mansoura University, Mansoura, Egypt.

|May 25, 2021

Summary

This summary is machine-generated.

This study introduces a novel financial crisis prediction model for small and medium-sized enterprises. It uses optimal feature selection and an optimized classification approach to accurately forecast business failure.

Keywords:

classification enterprises feature selection financial crisis prediction machine learning

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Area of Science:

Business Analytics
Computational Finance
Machine Learning

Background:

Accurate forecasting of business failure and financial crises is crucial for small- to medium-sized enterprises (SMEs).
Existing prediction models may lack the precision required for timely intervention.
The integration of advanced computational techniques offers potential for improved financial risk assessment.

Purpose of the Study:

To develop and validate an optimal feature selection (FS)-based classification model for financial crisis prediction (FCP) in SMEs.
To enhance the accuracy and reliability of financial distress forecasting.
To provide an effective tool for early detection of potential business failures.

Main Methods:

Data acquisition using Internet of Things (IoT) devices.
Pigeon-Inspired Optimization (PIO) for optimal feature selection.
Extreme Gradient Boosting (XGB) classification optimized by Jaya Optimization (JO) algorithm (JO-XGB).

Main Results:

The proposed PIO-JO-XGBoost model demonstrated superior performance in financial crisis prediction.
Experimental validation confirmed the model's effectiveness compared to existing methods.
The feature selection and optimized classification significantly improved prediction accuracy.

Conclusions:

The developed PIO-JO-XGBoost model is an effective tool for financial crisis prediction in SMEs.
The combination of PIO for FS and JO for XGBoost optimization enhances predictive capabilities.
This approach offers a robust solution for mitigating financial risks in businesses.