A descriptive study of variable discretization and cost-sensitive logistic regression on imbalanced credit data

Interaction effect between data discretization and data resampling for class-imbalanced medical datasets

BackgroundData discretization is an important preprocessing step in data mining for the transfer of continuous feature values to discrete ones, which allows some specific data mining algorithms to construct more effective models and facilitates the data mining process. Because many medical domain datasets are class imbalanced, data resampling methods, including oversampling, undersampling, and hybrid sampling methods, have been widely applied to rebalance the training set, facilitating effective differentiation between majority and minority classes.ObjectiveHerein, we examine the effect of incorporating both data discretization and data resampling as steps in the analytical process on the classifier performance for class-imbalanced medical datasets. The order in which these two steps are carried out is compared in the experiments.MethodsTwo experimental studies were conducted, one based on 11 two-class imbalanced medical datasets and the other using 3 multiclass imbalanced medical datasets. In addition, the two discretization algorithms employed are ChiMerge and minimum description length principle (MDLP). On the other hand, the data resampling algorithms chosen for performance comparison are Tomek links undersampling, synthetic minority oversampling technique (SMOTE) oversampling, and SMOTE-Tomek hybrid sampling algorithms. Moreover, the support vector machine (SVM), C4.5 decision tree, and random forest (RF) techniques were used to examine the classification performances of the different approaches.ResultsThe results show that on average, the combination approaches can allow the classifiers to provide higher area under the ROC curve (AUC) rates than the best baseline approach at approximately 0.8%-3.5% and 0.9%-2.5% for twoclass and multiclass imbalanced medical datasets, respectively. Particularly, the optimal results for two-class imbalanced datasets are obtained by performing the MDLP method first for data discretization and SMOTE second for oversampling, providing the highest AUC rate and requiring the least computational cost. For multiclass imbalanced datasets, performing SMOTE or SMOTE-Tomek first for data resampling and ChiMerge second for data discretization offers the best performances.ConclusionsClassifiers with oversampling can provide better performances than the baseline method without oversampling. In contrast, performing data discretization does not necessarily make the classifiers outperform the baselines. On average, the combination approaches have potential to allow the classifiers to provide higher AUC rates than the best baseline approach.

Optimization of Imbalanced and Multidimensional Learning Under Bayes Minimum Risk and Savings Measure

The full potential of data analysis is crippled by imbalanced and high-dimensional data, which makes these topics significantly important. Consequently, substantial research efforts have been directed to obtain dimension reduction and resolve data imbalance, especially in the context of fraud detection analysis. This work aims to investigate the effectiveness of hybrid learning methods for alleviating the class imbalance and integrating dimensionality reduction techniques. In this regard, the current study examines different classification combinations to achieve optimal savings and improve classification performance. Against this background, several well-known machine learning models are selected such as logistic regression, random forest, CatBoost (CB), and XGBoost. These models are constructed and optimized based on Bayes minimum risk (BMR) associated with the oversampling method synthetic minority oversampling technique (SMOTE) and different feature selection (FS) techniques, both univariate and multivariate. To investigate the performance of the proposed approach, different possible scenarios are analyzed both with and without balancing, with and without FS, and optimization using BMR. With a major insight about the best method to use, BMR shows a good optimization when used with SMOTE, symmetrical uncertainty for FS, and CB as a boosted classifier, principally in terms of F1 score and savings metrics.

Differential Replication for Credit Scoring in Regulated Environments

Differential replication is a method to adapt existing machine learning solutions to the demands of highly regulated environments by reusing knowledge from one generation to the next. Copying is a technique that allows differential replication by projecting a given classifier onto a new hypothesis space, in circumstances where access to both the original solution and its training data is limited. The resulting model replicates the original decision behavior while displaying new features and characteristics. In this paper, we apply this approach to a use case in the context of credit scoring. We use a private residential mortgage default dataset. We show that differential replication through copying can be exploited to adapt a given solution to the changing demands of a constrained environment such as that of the financial market. In particular, we show how copying can be used to replicate the decision behavior not only of a model, but also of a full pipeline. As a result, we can ensure the decomposability of the attributes used to provide explanations for credit scoring models and reduce the time-to-market delivery of these solutions.

Credit Risk Model Based on Central Bank Credit Registry Data

Data science and machine-learning techniques help banks to optimize enterprise operations, enhance risk analyses and gain competitive advantage. There is a vast amount of research in credit risk, but to our knowledge, none of them uses credit registry as a data source to model the probability of default for individual clients. The goal of this paper is to evaluate different machine-learning models to create accurate model for credit risk assessment using the data from the real credit registry dataset of the Central Bank of Republic of North Macedonia. We strongly believe that the model developed in this research will be an additional source of valuable information to commercial banks, by leveraging historical data for all the population of the country in all the commercial banks. Thus, in this research, we compare five machine-learning models to classify credit risk data, i.e., logistic regression, decision tree, random forest, support vector machines (SVM) and neural network. We evaluate the five models using different machine-learning metrics, and we propose a model based on credit registry data from the central bank with detailed methodology that can predict the credit risk based on credit history of the population in the country. Our results show that the best accuracy is achieved by using decision tree performing on imbalanced data with and without scaling, followed by random forest and linear regression.