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 45
- Downloads 0
Topological Data Analysis of COVID-19 Using Artificial Intelligence and Machine Learning Techniques in Big Datasets of Hausdorff Spaces
In this paper, we carry out an in-depth topological data analysis (TDA) of COVID-19 pandemic using artificial intelligence (AI) and Machine Learning (ML) techniques. We show the distribution patterns of pandemic all over...
Voice Biomarkers for Parkinson's Disease Prediction Using Machine Learning Models with Improved Feature Reduction Techniques
As a chronic and life-threatening disease, Parkinson’s disease (PD) causes people to become rigid and inactive and have shaky voices. There is an argument that current PD detection techniques are ineffective due to their...
Bootstrap Methods for Canonical Correlation Analysis of Functional Data
The bootstrap method is a very general resampling procedure for investigating the distributional property of statistics. In this paper, we present two bootstrap methods with the aim of studying the functional canonical c...
Identification of Damage in a Wind Turbine Blade Using Mechanical Measurements and Artificial Neural Networks
Due to the stochastic nature of environmental loadings, a lot of interest is paid in the discovery of possible damages to the involved equipment in modern industry. In wind turbines' blades, the development of a smart st...
Analytic Network Process (ANP) Method: A Comprehensive Review of Applications, Advantages, and Limitations
Nowadays, multi-criteria decision-making (MCDM) methods possess manifold applications in many areas from engineering to supply chain and management. The analytic network process (ANP) method is one of the most widely use...