Improving risk predictions by preprocessing imbalanced credit data

Authors:
Vicente García;Ana Isabel Marqués;Jose Salvador Sánchez
Affiliations:
Dep. Computer Languages and Systems - Institute of New Imaging Technologies, Universitat Jaume I, Castelló de la Plana, Spain;Dep. Business Administration and Marketing, Universitat Jaume I, Castelló de la Plana, Spain;Dep. Computer Languages and Systems - Institute of New Imaging Technologies, Universitat Jaume I, Castelló de la Plana, Spain
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Year:
2012

Citing 13
Cited 0

Credit Scoring and Its Applications

Credit Scoring and Its Applications
Improving Identification of Difficult Small Classes by Balancing Class Distribution

AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
A study of the behavior of several methods for balancing machine learning training data

ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Minority report in fraud detection: classification of skewed data

ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Statistical Comparisons of Classifiers over Multiple Data Sets

The Journal of Machine Learning Research
The class imbalance problem: A systematic study

Intelligent Data Analysis
Safe-Level-SMOTE: Safe-Level-Synthetic Minority Over-Sampling TEchnique for Handling the Class Imbalanced Problem

PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
SMOTE: synthetic minority over-sampling technique

Journal of Artificial Intelligence Research
Comparative Study on Class Imbalance Learning for Credit Scoring

HIS '09 Proceedings of the 2009 Ninth International Conference on Hybrid Intelligent Systems - Volume 02
Solving Credit Scoring Problem with Ensemble Learning: A Case Study

KAM '09 Proceedings of the 2009 Second International Symposium on Knowledge Acquisition and Modeling - Volume 01
Learning without default: a study of one-class classification and the low-default portfolio problem

AICS'09 Proceedings of the 20th Irish conference on Artificial intelligence and cognitive science
An experimental comparison of classification algorithms for imbalanced credit scoring data sets

Expert Systems with Applications: An International Journal
The condensed nearest neighbor rule (Corresp.)

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

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.