Evaluation of neural networks and data mining methods on a credit assessment task for class imbalance problem

Huang, Yueh Min; Hung, Chun Min; Jiau, Hewijin Christine

doi:10.1016/j.nonrwa.2005.04.006

Cited by 141 publications

(64 citation statements)

References 10 publications

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“…For example, Vinciotti and Hand [18] introduced a modification to logistic regression by taking into account the misclassification costs when the probability estimates are made. Huang et al [8] proposed two strategies for classification and cleaning of skewed credit data. One method involves randomly selecting instances to balance the proportion of examples in each class, whereas the second consists of combining the ID3 decision tree with a filter.…”

Section: Introductionmentioning

confidence: 99%

Improving Risk Predictions by Preprocessing Imbalanced Credit Data

García

Marqués

Sánchez

2012

Neural Information Processing

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Abstract. Imbalanced credit data sets refer to databases in which the class of defaulters is heavily under-represented in comparison to the class of non-defaulters. This is a very common situation in real-life credit scoring applications, but it has still received little attention. This paper investigates whether data resampling can be used to improve the performance of learners built from imbalanced credit data sets, and whether the effectiveness of resampling is related to the type of classifier. Experimental results demonstrate that learning with the resampled sets consistently outperforms the use of the original imbalanced credit data, independently of the classifier used.

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Section: Introductionmentioning

confidence: 99%

Improving Risk Predictions by Preprocessing Imbalanced Credit Data

García

Marqués

Sánchez

2012

Neural Information Processing

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“…Recent developments dedicated to solve the problem of constructing decision models that classify credit applicants make use of modern machine learning techniques such as neural networks (West 2000), Gaussian processes (Huang 2011), SVMs ), or ensemble classifiers (Nanni and Lumini 2009). The modern learning methods indicate the necessity to deal with the imbalanced data issue (Huang et al 2006;Zięba and Swiątek 2012), as well as with the need of constructing the comprehensible predictors (Martens et al 2007). The short-term loans are typically easier to qualify for, both in terms of income and credit rating, than other types of credits.…”

Section: Descriptionmentioning

confidence: 99%

Boosted SVM with active learning strategy for imbalanced data

Ziăba

Tomczak

2014

Soft Comput

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In this work, we introduce a novel training method for constructing boosted Support Vector Machines (SVMs) directly from imbalanced data. The proposed solution incorporates the mechanisms of active learning strategy to eliminate redundant instances and more properly estimate misclassification costs for each of the base SVMs in the committee. To evaluate our approach, we make comprehensive experimental studies on the set of 44 benchmark datasets with various types of imbalance ratio. In addition, we present application of our method to the real-life decision problem related to the short-term loans repayment prediction.

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“…For example, Vinciotti and Hand (2003) introduced a modification to straightforward logistic regression by taking into account the misclassification costs when the probability estimates are made. Huang et al (2006) proposed two strategies for classification and cleaning of skewed credit data. One method involves randomly selecting instances to balance the proportion of examples in each class, whereas the second method consists of combining the ID3 decision tree and the PRISM filter.…”

Section: Class Imbalance In Credit Scoringmentioning

confidence: 99%

On the suitability of resampling techniques for the class imbalance problem in credit scoring

Marqués

García

Sánchez

2013

Journal of the Operational Research Society

124

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In real-life credit scoring applications, the case in which the class of defaulters is under-represented in comparison to the class of non-defaulters is a very common situation, but it has still received little attention. The present paper investigates the suitability and performance of several resampling techniques when applied in conjunction with statistical and artificial intelligence prediction models over five real-world credit data sets, which have artificially been modified to derive different imbalance ratios (proportion of defaulters and non-defaulters examples). Experimental results demonstrate that the use of resampling methods consistently improves the performance given by the original imbalanced data. Besides, it is also important to note that in general, over-sampling techniques perform better than any under-sampling approach.

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Evaluation of neural networks and data mining methods on a credit assessment task for class imbalance problem

Cited by 141 publications

References 10 publications

Improving Risk Predictions by Preprocessing Imbalanced Credit Data

Improving Risk Predictions by Preprocessing Imbalanced Credit Data

Boosted SVM with active learning strategy for imbalanced data

On the suitability of resampling techniques for the class imbalance problem in credit scoring

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