An Ensemble Stacking Algorithm to Improve Model Accuracy in Bankruptcy Prediction
Journal Title: Journal of Data Science and Intelligent Systems - Year 2024, Vol 2, Issue 2
Abstract
Bankruptcy analysis is needed to anticipate bankruptcy. Errors in predicting bankruptcy often cause bankruptcy. Machine learning with high accuracy to analyze reversal must continuously improve its accuracy. Many machine learning models have been applied to predict bankruptcy. However, model improvisation is still needed to improve prediction accuracy. We propose a combination model to improve the accuracy of bankruptcy prediction based on a genetic algorithm-support vector machine (GA-SVM) and stacking ensemble method. This study uses the Taiwanese Bankruptcy dataset from the Taiwan Economic Journal. Then we implement a synthetic minority over-sampling technique for handling imbalanced datasets. We select the best feature using GA-SVM, adopt a new strategy by stacking the classifier, and use extreme gradient boosting as a meta-learner. The results show superior accuracy obtained by the stacking model-based GA-SVM with an accuracy of 99.58%. The accuracy obtained is higher than just applying a single classifier. Thus, this study shows that the proposed method can predict bankruptcy with superior accuracy.
Authors and Affiliations
Much Aziz Muslim, Yosza Dasril, Haseeb Javed, Alamsyah, Jumanto, Wiena Faqih Abror, Dwika Ananda Agustina Pertiwi, Tanzilal Mustaqim
Keywords
Related Articles
- EP ID EP752180
- DOI 10.47852/bonviewJDSIS3202655
- Views 26
- Downloads 0
Performance Metrics of an Intrusion Detection System Through Window-Based Deep Learning Models
Intrusion and prevention technologies perform reliably in harsh conditions by fortifying many of the world's highest security sites with few defects in high performance. This paper aims to contribute by designing an intr...
Intra-annual National Statistical Accounts Based on Machine Learning Algorithm
The methods used for forecasting financial series are based on the concept that a pattern can be identified in the data and distinguished from randomness by smoothing past values. This smoothing process eliminates random...
Models and Techniques for Domain Relation Extraction: A Survey
As the significant subtask of information extraction, relation extraction (RE) aims to identify and classify semantic relations between pairs of entities and is widely adopted as the foundation of downstream applications...
Multiple Regression Model as Interpolation Through the Points of Weighted Means
A well-known property of the multiple linear regression is that its plane goes through the point of the mean values of all variables, and this feature can be used to find the model's intercept. This work shows that a re...
A Comparative Analysis of Feature Eliminator Methods to Improve Machine Learning Phishing Detection
This Machine-learning-based phishing detection employs statistical models and algorithms to assess and recognise phishing attacks. These algorithms can learn patterns and features that distinguish between phishing and no...