The nature of statistical learning theory
The nature of statistical learning theory
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Determining the saliency of input variables in neural network classifiers
Computers and Operations Research
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
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In the increasingly competitive credit industry, one of the most interesting and challenging problems is how to manage existing customers. Behavior scoring models have been widely used by financial institutions to forecast customer's future credit performance. In this paper, a hybrid GA+SVM model, which uses genetic algorithm (GA) to search the promising subsets of features and multi-class support vector machines (SVM) to make behavior scoring prediction, is presented. A real life credit data set in a major Chinese commercial bank is selected as the experimental data to compare the classification accuracy rate with other traditional behavior scoring models. The experimental results show that GA+SVM can obtain better performance than other models.