This research aims to propose a framework for evaluating credit applications by assigning a binary score to the applicant. The score is targeted to determine whether the credit application is ‘good’ or ‘bad’ in small business purpose loans. Even tiny performance improvements in small businesses may yield a positive impact on the economy as they generate more than 60% of the value. The method presented in this paper hybridizes the Genetic Algorithm (GA) and the Support Vector Machine (SVM) in a bi-level feeding mechanism for increased prediction accuracy. The first level is to determine the parameters of SVM and the second is to find a feature set that increases classification accuracy. To test the proposed approach, we have investigated three different data sets; UCI Australian data set for preliminary works, Lending Club data set for large training and testing, and UCI German and Australian datasets for benchmarking against some other notable methods that use GA. Our computational results show that our proposed method using a feedback mechanism under the hybrid bi-level GA-SVM structure outperforms other classification algorithms in the literature, namely Decision Tree, Random Forests, Logistic Regression, SVM and Artificial Neural Networks, effectively improves the classification accuracy.

中文翻译：

---

混合双层元启发式评分

这项研究旨在提出一种通过为申请人分配二进制分数来评估信用申请的框架。该分数的目标是确定小企业贷款中的信用申请是“好”还是“坏”。由于小型企业创造的价值超过60％，即使是微小的绩效改善也可能对经济产生积极影响。本文提出的方法将遗传算法（GA）和支持向量机（SVM）混合在双层进给机制中，以提高预测精度。第一级是确定SVM的参数，第二级是找到可提高分类准确性的功能集。为了测试提出的方法，我们研究了三个不同的数据集。UCI澳大利亚数据集用于前期工作，Lending Club数据集用于大型培训和测试，UCI德国和澳大利亚数据集用于与使用GA的其他一些著名方法进行基准比较。计算结果表明，在混合双层GA-SVM结构下使用反馈机制提出的方法优于文献中的其他分类算法，即决策树，随机森林，Logistic回归，SVM和人工神经网络，有效地改善了分类准确性。