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A Descriptive Study Of Variable Discretization And Cost-Sensitive Logistic Regression On Imbalanced Credit Data, Lili Zhang, Jennifer Priestley, Herman Ray, Soon Tan
A Descriptive Study Of Variable Discretization And Cost-Sensitive Logistic Regression On Imbalanced Credit Data, Lili Zhang, Jennifer Priestley, Herman Ray, Soon Tan
Published and Grey Literature from PhD Candidates
Training classification models on imbalanced data tends to result in bias towards the majority class. In this paper, we demonstrate how variable discretization and cost-sensitive logistic regression help mitigate this bias on an imbalanced credit scoring dataset, and further show the application of the variable discretization technique on the data from other domains, demonstrating its potential as a generic technique for classifying imbalanced data beyond credit scoring. The performance measurements include ROC curves, Area under ROC Curve (AUC), Type I Error, Type II Error, accuracy, and F1 score. The results show that proper variable discretization and cost-sensitive logistic regression with …
A Comparison Of Machine Learning Algorithms For Prediction Of Past Due Service In Commercial Credit, Liyuan Liu M.A, M.S., Jennifer Lewis Priestley Ph.D.
A Comparison Of Machine Learning Algorithms For Prediction Of Past Due Service In Commercial Credit, Liyuan Liu M.A, M.S., Jennifer Lewis Priestley Ph.D.
Published and Grey Literature from PhD Candidates
Credit risk modeling has carried a variety of research interest in previous literature, and recent studies have shown that machine learning methods achieved better performance than conventional statistical ones. This study applies decision tree which is a robust advanced credit risk model to predict the commercial non-financial past-due problem with better critical power and accuracy. In addition, we examine the performance with logistic regression analysis, decision trees, and neural networks. The experimenting results confirm that decision trees improve upon other methods. Also, we find some interesting factors that impact the commercials’ non-financial past-due payment.
Influence Of The Event Rate On Discrimination Abilities Of Bankruptcy Prediction Models, Lili Zhang, Jennifer Priestley, Xuelei Ni
Influence Of The Event Rate On Discrimination Abilities Of Bankruptcy Prediction Models, Lili Zhang, Jennifer Priestley, Xuelei Ni
Published and Grey Literature from PhD Candidates
In bankruptcy prediction, the proportion of events is very low, which is often oversampled to eliminate this bias. In this paper, we study the influence of the event rate on discrimination abilities of bankruptcy prediction models. First the statistical association and significance of public records and firmographics indicators with the bankruptcy were explored. Then the event rate was oversampled from 0.12% to 10%, 20%, 30%, 40%, and 50%, respectively. Seven models were developed, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and Neural Network. Under different event rates, models were comprehensively evaluated and compared …