Articlegold2019

A Novel Noise-Adapted Two-Layer Ensemble Model for Credit Scoring Based on Backflow Learning

Shuang Wei · Dongqi Yang · Wenyu Zhang · Shuai Zhang

36 citations0/0 files storedIEEE Access

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Financial Distress and Bankruptcy Prediction Machine Learning and Data Classification Imbalanced Data Classification Techniques

Abstract

Recently, the machine learning method and artificial intelligence algorithm have become increasingly important in classification problems, such as credit scoring. Building an ensemble learning model that has been proven to be typically more accurate and robust than individual classifiers, it is an important information management task of commercial banks and loan lenders. In this paper, a novel noise-adapted two-layer ensemble model for credit scoring based on backflow learning is proposed, in which five widely used base classifiers, i.e., extreme gradient boosting, gradient boosting decision tree, support vector machine, random forest, and linear discriminant analysis, are integrated. To amplify the strength and diversity of the base classifiers, a new backflow learning approach is proposed so that the base classifiers will relearn the misclassified data point. A final predictive result is obtained by fusing the prediction of all base classifiers through two-layer ensemble modeling. In addition, considering that noise data are a major problem that aggravates the accuracy of a predictive model, a new noise adaption approach based on the isolation forest algorithm is proposed to address noise data. It first calculates the outlier score of each data point to detect the noise data that are subsequently boosted in the training set to form the noise-adapted training set. Three credit datasets from the UCI machine learning repository are tested to compare the performance of the proposed model with those of other benchmark models. The experimental results prove that our proposed model outperforms other models by demonstrating satisfactory improvement in various performance measures.

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References

2015Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research 2003Credit rating analysis with support vector machines and neural networks: a market comparative study 2010A comparative assessment of ensemble learning for credit scoring 2007Recent developments in consumer credit risk assessment 2016Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction 2004Mining the customer credit using classification and regression tree and multivariate adaptive regression splines 2004Neural network ensemble strategies for financial decision applications 2011Machine Learning in Financial Crisis Prediction: A Survey

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2024Advancing Bankruptcy Forecasting With Hybrid Machine Learning Techniques: Insights From an Unbalanced Polish Dataset 2024Enhancing credit scoring accuracy with a comprehensive evaluation of alternative data 2020A Novel GSCI-Based Ensemble Approach for Credit Scoring 2021Improved credit scoring model using XGBoost with Bayesian hyper-parameter optimization

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