Data mining feature selection for credit scoring models

Data mining feature selection for credit scoring models

0.00 Avg rating0 Votes
Article ID: iaor20062730
Country: United Kingdom
Volume: 56
Issue: 9
Start Page Number: 1099
End Page Number: 1108
Publication Date: Sep 2005
Journal: Journal of the Operational Research Society
Authors: ,
Keywords: datamining
Abstract:

The features used may have an important effect on the performance of credit scoring models. The process of choosing the best set of features for credit scoring models is usually unsystematic and dominated by somewhat arbitrary trial. This paper presents an empirical study of four machine learning feature selection methods. These methods provide an automatic data mining technique for reducing the feature space. The study illustrates how four feature selection methods, ‘ReliefF’, ‘Correlation-based’, ‘Consistency-based’ and ‘Wrapper’ algorithms, help to improve three aspects of the performance of scoring models: model simplicity, model speed and model accuracy. The experiments are conducted on real data sets using four classification algorithms – ‘model tree (M5)’, ‘neural network (multi-layer perceptron with back-propagation)’, ‘logistic regression’, and ‘k-nearest-neighbours’.

Reviews

Required fields are marked *. Your email address will not be published.