Articlehybrid2024

Comparison of the impact of dimensionality reduction and data splitting on classification performance in credit risk assessment

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

Abstract

Credit risk assessment (CRA) plays an important role in credit decision-making process of financial institutions. Today, developing big data analysis and machine learning methods have marked a new era in credit risk estimation. In recent years, using machine learning methods in credit risk estimation has emerged as an alternative method for financial institutions. The past demographic and financial data of the person whose CRA will be performed is important for creating an automatic artificial intelligence credit score prediction model based on machine learning. It is also necessary to use features correctly to create accurate machine learning models. This article aims to investigate the effects of dimensionality reduction and data splitting steps on the performance of classification algorithms widely used in the literature. In our study, dimensionality reduction was performed using Principal Component Analysis (PCA). Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), Naive Bayes (NB) algorithms were chosen for classification. Percentage splitting (PER, 66–34%) and k-fold (k = 10) cross-validation techniques were used when dividing the data set into training and test data. The results obtained were evaluated with accuracy, recall, F1 score, precision, and AUC metrics. German data set was used in this study. The effect of data splitting and dimension reduction on the classification of CRA systems was examined. The highest ACC in PER and CV data splitting was obtained with the RF algorithm. Using data splitting methods and PCA, the highest accuracy was observed with RF and the highest AUC with NB, with 13 PCs in which 80% of the variance was obtained. As a result, the data set consisting of a total of 20 features, expressed by 13 PCs, achieved similar or higher success than the results obtained from the original data set.

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References

2015Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research 2016Big data preprocessing: methods and prospects 2020A benchmark of machine learning approaches for credit score prediction 2016A comparative study on base classifiers in ensemble methods for credit scoring 2014Investigation and improvement of multi-layer perceptron neural networks for credit scoring 2016A deep learning approach for credit scoring using credit default swaps 2013Consumer credit risk: Individual probability estimates using machine learning 2019A study on predicting loan default based on the random forest algorithm

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