Improving Risk Predictions by Preprocessing Imbalanced Credit Data

García, Vicente; Marqués, A. I.; Sánchez, J. Salvador

doi:10.1007/978-3-642-34481-7_9

Cited by 16 publications

(17 citation statements)

References 17 publications

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“…Hence, most credit scoring algorithms have adopted a re-sampling approach [ 21 , 28 – 32 ]. In [ 21 , 28 ], simple over-sampling or under-sampling approaches are applied and the experiments in [ 29 , 30 ] provide evidence that over-sampling is superior to under-sampling in terms of accuracy. The results in [ 31 ] also reveal that over-sampling outperforms under-sampling in most cases, especially with the logistic regression model.…”

Section: Introductionmentioning

confidence: 99%

Large Unbalanced Credit Scoring Using Lasso-Logistic Regression Ensemble

Hong

Zhou

2015

PLoS ONE

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Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. In this paper, given large unbalanced data, we consider the plausibility of ensemble learning using regularized logistic regression as the base classifier to deal with credit scoring problems. In this research, the data is first balanced and diversified by clustering and bagging algorithms. Then we apply a Lasso-logistic regression learning ensemble to evaluate the credit risks. We show that the proposed algorithm outperforms popular credit scoring models such as decision tree, Lasso-logistic regression and random forests in terms of AUC and F-measure. We also provide two importance measures for the proposed model to identify important variables in the data.

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Section: Introductionmentioning

confidence: 99%

Large Unbalanced Credit Scoring Using Lasso-Logistic Regression Ensemble

Hong

Zhou

2015

PLoS ONE

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“…Although SMOTE has proved to be an effective tool for handling the class imbalancement, it may overgeneralize the minority class, once it does not consider the distribution of majority class neighbors. As a result, it may increase the overlapping between classes [17].…”

Section: Literature Rev Iewmentioning

confidence: 99%

An evolutionary sampling approach for classification with imbalanced data

Fernandes

Carvalho

Coelho

2015

2015 International Joint Conference on Neural Networks (IJCNN)

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Abstract-In some practical classification problems in which the number of instances of a particular class is much lower/higher than the instances of the other classes, one commonly adopted strategy is to train the classifier over a small, balanced portion of the training data set. Although straightforward, this procedure may discard instances that could be important for the better discrimination of the classes, affecting the performance of the resulting classifier. To address this problem more properly, in this paper we present MOGASamp (after Multiobjective Genetic Sampling) as an adaptive approach that evolves a set of samples The overall results show that the proposed method have achieved a noticeable performance in terms of accuracy measures.

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“…A new training dataset containing a balanced number of positive and negative examples is created. Furthermore, recently the synthetic minority over-sampling technique (SMOTE) [23] has also been applied to credit scoring [24]. With SMOTE the objective is to find Average increase in savings of the algorithms trained using the under-sampled, SMOTE, cost-proportionate rejection-sampling and costproportionate over-sampling compared against the ones trained in the training set.…”

Section: B Database Partitioningmentioning

confidence: 99%

Example-Dependent Cost-Sensitive Logistic Regression for Credit Scoring

Bahnsen

Aouada

Ottersten

2014

2014 13th International Conference on Machine Learning and Applications

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Abstract-Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples. Credit scoring is a typical example of cost-sensitive classification. However, it is usually treated using methods that do not take into account the real financial costs associated with the lending business. In this paper, we propose a new example-dependent cost matrix for credit scoring. Furthermore, we propose an algorithm that introduces the example-dependent costs into a logistic regression. Using two publicly available datasets, we compare our proposed method against state-of-the-art example-dependent cost-sensitive algorithms. The results highlight the importance of using real financial costs. Moreover, by using the proposed cost-sensitive logistic regression, significant improvements are made in the sense of higher savings.

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Improving Risk Predictions by Preprocessing Imbalanced Credit Data

Cited by 16 publications

References 17 publications

Large Unbalanced Credit Scoring Using Lasso-Logistic Regression Ensemble

Large Unbalanced Credit Scoring Using Lasso-Logistic Regression Ensemble

An evolutionary sampling approach for classification with imbalanced data

Example-Dependent Cost-Sensitive Logistic Regression for Credit Scoring

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