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
Computers and Industrial Engineering
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Scatter Search: Methodology and Implementations in C
Scatter Search: Methodology and Implementations in C
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
Granular computing, rough entropy and object extraction
Pattern Recognition Letters
Tabu search for attribute reduction in rough set theory
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Scatter Search for Rough Set Attribute Reduction
CSO '09 Proceedings of the 2009 International Joint Conference on Computational Sciences and Optimization - Volume 01
A global search algorithm for attributes reduction
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
On the use of data filtering techniques for credit risk prediction with instance-based models
Expert Systems with Applications: An International Journal
Investigating memetic algorithm in solving rough set attribute reduction
International Journal of Computer Applications in Technology
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As the credit industry has been growing rapidly, credit scoring models have been widely used by the financial industry during this time to improve cash flow and credit collections. However, 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, called RSFS, to feature selection based on rough set and scatter search is proposed. In RSFS, conditional entropy is regarded as the heuristic to search the optimal solutions. Two credit datasets in UCI database are selected to demonstrate the competitive performance of RSFS consisted in three credit models including neural network model, J48 decision tree and Logistic regression. The experimental result shows that RSFS has a superior performance in saving the computational costs and improving classification accuracy compared with the base classification methods.