Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Credit scoring with a data mining approach based on support vector machines
Expert Systems with Applications: An International Journal
Consumer credit scoring models with limited data
Expert Systems with Applications: An International Journal
Comparison procedure of predicting the time to default in behavioural scoring
Expert Systems with Applications: An International Journal
The consumer loan default predicting model - An application of DEA-DA and neural network
Expert Systems with Applications: An International Journal
Multiple classifier application to credit risk assessment
Expert Systems with Applications: An International Journal
The evaluation of consumer loans using support vector machines
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
The bank loan approval decision from multiple perspectives
Expert Systems with Applications: An International Journal
Hybrid genetic algorithms for stress recognition in reading
EvoBIO'13 Proceedings of the 11th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Genetic algorithm-based heuristic for feature selection in credit risk assessment
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
The databases of the banks around the world have accumulated large quantities of information about clients and their financial and payment history. These databases can be used for the credit risk assessment, but they are commonly high dimensional. Irrelevant features in a training dataset may produce less accurate results of classification analysis. Data preprocessing is required to prepare the data for classification to increase the predictive accuracy. Feature selection is a preprocessing technique commonly used on high dimensional data and its purposes include reducing dimensionality, removing irrelevant and redundant features, facilitating data understanding, reducing the amount of data needed for learning, improving predictive accuracy of algorithms, and increasing interpretability of models. In this paper we investigate the extent to which the total data, owned by a bank, can be a good basis for predicting the borrower's ability to repay the loan on time. We propose a feature selection technique for finding an optimum feature subset that enhances the classification accuracy of neural network classifiers. Experiments were conducted on the credit dataset collected at a Croatian bank to assess the accuracy of our technique. We found that the hybrid system with genetic algorithm is competitive and can be used as feature selection technique to discover the most significant features in determining risk of default.