Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
An introduction to variable and feature selection
The Journal of Machine Learning Research
Approaches to knowledge reduction based on variable precision rough set model
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
User-Oriented Feature Selection for Machine Learning
The Computer Journal
A comparative study of fuzzy sets and rough sets
Information Sciences: an International Journal
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As the credit industry has been growing rapidly, huge number of consumers' credit data are collected by the credit department of the bank and credit scoring has become a very important issue. Usually, a large amount of redundant information and features are involved in the credit dataset, which leads to lower accuracy and higher complexity of the credit scoring model, so, effective feature selection methods are necessary for credit dataset with huge number of features. In this paper, a novel approach to credit scoring feature selection based on fuzzy-rough model and information entropy is proposed. Three UCI credit datasets are selected to demonstrate the competitive performance of the presented method comparing with some other methods. Experiments show the proposed method get a better performance comparing with the classical rough set approaches.