Designing a Hybrid Intelligent Mining System for Credit Risk Evaluation

Abstract

In this study, a novel hybrid intelligent mining system integrating rough sets theory and support vector machines is developed to extract efficiently association rules from original information table for credit risk evaluation and analysis. In the proposed hybrid intelligent system, support vector machines are used as a tool to extract typical features and filter its noise, which are different from the previous studies where rough sets were only used as a preprocessor for support vector machines. Such an approach could reduce the information table and generate the final knowledge from the reduced information table by rough sets. Therefore, the proposed hybrid intelligent system overcomes the difficulty of extracting rules from a trained support vector machine classifier and possesses the robustness which 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.

Enterprise Credit Risk Evaluation models: A Review of Current Research Trends

“…In combination of two model, [70] used Rough set theory and GA-based SVM to evaluate credit risk. Some other techniques, such as integrating genetic programming and support vector machine [67], A intelligent system for credit risk evaluation [66], Combination of feature selection approaches and SVM.…”

Credit Risk Evaluation with a Least Squares Fuzzy Support Vector Machines Classifier

“…Recent examples are neural discriminant technique [23], neurofuzzy [16,24], fuzzy SVM [25], rough set based SVM [26], evolving neural network [27], neural network ensemble [28,29], support vector machine based multiagent ensemble learning [30], and AI-based fuzzy group decision making (GDM) model [31]. Two recent surveys [32,33] and one monograph [34] cover credit risk analysis in more detail.…”

Multiple Solutions of Second-Order Damped Impulsive Differential Equations with Mixed Boundary Conditions

“…Impulsive effect exists widely in many evolution processes in which their states are changed abruptly at certain moments of time. The theory of impulsive differential systems has been developed by numerous mathematicians [1][2][3][4][5][6]. Applications of impulsive differential equations with or without delays occur in biology, medicine, mechanics, engineering, chaos theory, and so on [7][8][9][10][11].…”