A comparative assessment of ensemble learning for credit scoring

By G. Wang, Jin-Xing Hao, Jian Ma, Hongbing Jiang. Expert systems with applications, 2011

484 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Finance

View Paper: https://www.semanticscholar.org/paper/d17e77d39add626e5a4a0e02a98296e275627619

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

1. Stefan Lessmann
2. Lyn C. Thomas
3. Hsin-Vonn Seow
4. Yufei Xia
5. Flavio Luiz de Moraes Barboza
6. Hamido Fujita
7. Jie Lang
8. Chuanzhe Liu

In Artificial Intelligence

1. Stefan Lessmann
2. Lyn C. Thomas
3. Hsin-Vonn Seow
4. Yufei Xia
5. Chuanzhe Liu
6. Yuying Li
7. Nana Liu
8. Flavio Luiz de Moraes Barboza

In Finance

1. Yufei Xia
2. Flavio Luiz de Moraes Barboza
3. Stefan Lessmann
4. Hamido Fujita
5. Jie Lang
6. Chuanzhe Liu
7. Lyn C. Thomas
8. Bart Baesens

Citations in the corpus

• Imbalanced enterprise credit evaluation with DTE-SBD: Decision tree ensemble based on SMOTE and bagging with differentiated sampling rates (2018, result): “Wang et al. (2011) compare the three ensemble learning methods of Bagging, Boosting and Stacking for credit evaluation, and find that multiple-classifier ensemble can significantly improve the performance of enterprise credit evaluation [36].”
• Classification methods applied to credit scoring: A systematic review and overall comparison (2016, unknown): “…Lee et al. (2006); Huang et al. (2006b); Xiao et al. (2006); Van Gestel et al. (2007); Martens et al. (2007); Hu and Ansell (2007); Tsai (2008); Abdou et al. (2008); Sinha and Zhao (2008); Luo et al. (2009); Finlay (2009); Abdou (2009); Hu and Ansell (2009); Finlay (2010); Wang et al. (2011).”
• A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring (2017, methodology): “In the field of credit scoring, a few research have focused on AdaBoost with DT and NN as base models (Tsai et al., 2014; Twala, 2010; Wang et al., 2011).”
• How Many Trees in a Random Forest? (2012, background): “The vote of each individual learner is weighted proportionally to its performance [31].”
• Machine learning models and bankruptcy prediction (2017, methodology): “Furthermore, Wang et al. (2011) used ensemble methods (bagging, boosting, and stacking) coupled 39 with base learners (logistic regression, decision trees, ANN, and SVM) to find that bagging outperformed 40”
• Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research (2015, background): “For example, a larger number of classifiers can be the result of factorial designs that pair some classification algorithms with some homogeneous ensemble algorithms (e.g., Marqués et al., 2012a; Wang et al., 2011).”