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Binary BAT algorithm and RBFN based hybrid credit scoring model
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DOI：10.1007/s11042-020-09538-6.png)
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摘要
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Credit scoring is a process of calculating the risk associated with an applicant on the basis of applicant's credentials such as social status, financial status, etc. and it plays a vital role to improve cash flow for financial industry. However, the credit scoring dataset may have a large number of irrelevant or redundant features which leads to poorer classification performances and higher complexity. So, by removing redundant and irrelevant features may overcome the problem with huge number of features. This work emphasized on the role of feature selection and proposed a hybrid model by combining feature selection by utilizing Binary BAT optimization technique with a novel fitness function and aggregated with for Radial Basis Function Neural Network (RBFN) for credit score classification. Further, proposed feature selection approach is aggregated with Support Vector Machine (SVM) & Random Forest (RF), and other optimization approaches namely: Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), Hybrid Particle Swarm Optimization and Genetic Algorithm (PSOGA), Improved Krill Herd (IKH), Improved Cuckoo Search (ICS), Firefly Algorithm (FF) and Differential Evolution (DE) are also applied for comparative analysis.
Keyword:
Optimization
Credit scoring
Feature selection
Classification
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3
论文数:
1.9W
被引数:
3.2W
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Particle swarm optimization for parameter determination and feature selection of support vector machines粒子群优化算法在支持向量机参数确定与特征选择中的应用
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