Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering

Jabeur, Sami Ben; Stef, Nicolae; Ibáñez, Pedro Carmona

doi:10.1007/s10614-021-10227-1

Cited by 83 publications

(46 citation statements)

References 89 publications

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“…XGBoost integrates the decision trees with gradient boosting mechanism. At each training round of a tree (weak learner), the residual of the previous tree is used in the next tree to minimize the loss function [33]. XGBoost also avoids overfitting problem and minimizes the computational complexity.…”

Section: • Adaptive Boostingmentioning

confidence: 99%

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Employing a Machine Learning Boosting Classifiers Based Stacking Ensemble Model for Detecting Non Technical Losses in Smart Grids

et al. 2022

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In the modern world, there are numerous opportunities that help in the detection of electricity theft happening in the realm of electricity grids due to the widespread shifting of people from old metering infrastructure to advanced metering infrastructure (AMI). It is done by studying the consumers' energy consumption (EC) readings provided by the smart meters (SM). The literature introduces a variety of machine learning (ML) and deep learning (DL) strategies to use EC data for identifying power theft in smart grids (SGs). However, the existing schemes provide low performance in terms of electricity theft detection (ETD) due to the usage of imbalanced data and using schemes in an individual manner. Moreover, the existing detectors are validated using a limited number of performance evaluation measures, which are not suitable for conducting model's comprehensive validation. To tackle the above mentioned problems, an ML boosting classifiers based stacking ensemble model (MLBCSM) is proposed followed by adaptive synthetic sampling technique (ADASYN) in the underlying work. Data preprocessing followed by data balancing and classification are the three major parts of the model introduced in this work. Besides, the EC data acquired from the consumers' SMs is used for detecting electricity theft. Moreover, the simulation results reveal that MLBCSM combines the benefits of adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), histogram boosting (HistBoost), categorical boosting (CatBoost), and light gradient boosting (LGBoost). Additionally, the model's validation is ensured via different metrics. It is deduced via extensive simulations that the proposed model's outcomes are superior to those produced by the individual models in terms of ETD.

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Section: • Adaptive Boostingmentioning

confidence: 99%

“…The final classification result, at the end, is acquired by combining all the weak learners, i.e., decision trees. The final output is predicted using Equation 11 [33].…”

Section: • Adaptive Boostingmentioning

confidence: 99%

Employing a Machine Learning Boosting Classifiers Based Stacking Ensemble Model for Detecting Non Technical Losses in Smart Grids

et al. 2022

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“…Based on their findings, the authors conclude that lasso regression effectively deals with high-dimensional features and the class-imbalance problem simultaneously. Jabeur et al (2022) showed that embedded methods achieved better effectiveness and have a more solid approach to discriminate the observations than standard wrapper methods such as stepwise DA or stepwise LR. In contrast, Zoričák et al (2020) identified a gap in recent studies that would analyze the application of feature selection methods for imbalanced datasets.…”

Section: Impacts Of Resampling and Feature Selection On Bankruptcy Pr...mentioning

confidence: 99%

“…(Boughaci & Alkhawaldeh, 2020;Kuppili et al, 2019;Le, Vo, Fujita, et al, 2019;Lin et al, 2019;Roumani et al, 2020;Uthayakumar et al, 2020) The predictive power of bankruptcy models depends on the number of variables only to a certain extent. Divsalar et al (2012) However, some researchers (Jabeur et al, 2022;Ragab & Saleh, 2022;Tang et al, 2020;Zelenkov et al, 2017) variety of nonfinancial variables is unavailable or very difficult to obtain as there is no cost-effective possibility to collect these data.…”

Section: Predictive Features For Bankruptcy Predictionmentioning

confidence: 99%

“…Other validation methods, such as leave-one-out cross-validation or holdout cross-validation, were not applied in this study as they are not applicable to studies with large data samples (Molinaro et al, 2005). Even though some studies (Boughaci & Alkhawaldeh, 2020;Jabeur et al, 2021Jabeur et al, , 2022Soui et al, 2020) used holdout cross-validation, most researchers (Farooq & Qamar, 2019;Hajek et al, 2014;Lahmiri et al, 2020;Le, 2022;Liang et al, 2015;Tang et al, 2020;Uthayakumar et al, 2020;Wang et al, 2018) prefer k-fold cross-validation, especially 10-fold validation, as it reduces the bias and variance.…”

Section: Such a Data Sample Size Represents Almost A Half Of All Smes Inmentioning

confidence: 99%

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Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium‐sized enterprises

Papíková

Papík

2022

Intell Sys Acc Fin Mgmt

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Small and medium-sized enterprises are the pillars of an economy, and their poor performance has a negative impact on living standards of population and country development. This study analyzes real-life data of 89,851 small and medium-sized enterprises, out of which 295 have declared bankruptcy. The analysis is performed via 27 financial ratios. The study framework combines seven classifications and three resampling and seven feature selection methods. Out of all classification methods applied, CatBoost has achieved the best results for all combinations of resampling and feature selection methods. CatBoost surpassed the results of other classification methods for the area under curve parameter, achieving a value of 99.95%. The application of resampling methods on different classification models has not identified a statistically significant level of improvement in any of the resampling methods. This finding has also been observed for feature selection methods. Based on these findings, we assume that individual resampling and feature selection methods do not improve model performance compared with the original imbalanced sample's results.Our results suggest that, even though the data sample may be significantly imbalanced with a minority of bankrupt companies, most classification algorithms can handle this imbalance and achieve interesting results. Moreover, our findings provide broad practical application for all stakeholders who could need to detect bankrupting companies.

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Intelligent Feature Engineering and Feature Selection Techniques for Machine Learning Evaluation

Ramesh,

kushwaha,

Sharma

et al. 2024

Lecture Notes in Networks and Systems

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Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering

Cited by 83 publications

References 89 publications

Employing a Machine Learning Boosting Classifiers Based Stacking Ensemble Model for Detecting Non Technical Losses in Smart Grids

Employing a Machine Learning Boosting Classifiers Based Stacking Ensemble Model for Detecting Non Technical Losses in Smart Grids

Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium‐sized enterprises

Intelligent Feature Engineering and Feature Selection Techniques for Machine Learning Evaluation

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