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In this study,a novel hybrid intelligent mining system integrating rough sets theory andsupport vector machines is developed to extract efficiently association rules from original informationtable for credit risk evaluation and analysis.In the proposed hybrid intelligent system,support vectormachines are used as a tool to extract typical features and filter its noise,which are different from theprevious studies where rough sets were only used as a preprocessor for support vector machines.Suchan approach could reduce the information table and generate the final knowledge from the reducedinformation table by rough sets.Therefore,the proposed hybrid intelligent system overcomes the diffi-culty of extracting rules from a trained support vector machine classifier and possesses the robustnesswhich is lacking for rough-set-based approaches.In addition,the effectiveness of the proposed hybridintelligent system is illustrated with two real-world credit datasets.
In this study, a novel hybrid intelligent mining system integrating rough sets theory and support vector machines is developed to extract efficient association rules from original informationtable for credit risk evaluation and analysis. The proposed hybrid intelligent system, support vectormachines are used as a tool to extract typical features and filter its noise, which are different from theprevious studies where rough sets were only used as a preprocessor for support vector machines.Suchan approach could reduce the information table and generate the final knowledge from the reducedinformation table by rough sets.Therefore, the proposed hybrid intelligent system overcomes the diffi-culty of extracting rules from a trained support vector machine classifier and possesses the robustnesswhich is lacking for rough-set-based approaches.In addition, the effectiveness of the proposed hybrid intelligent system is illustrated with two real-world credit datasets.