Nowadays, the effective credit scoring becomes a very crucial factor for gaining competitive advantages in credit market for both customers and corporations. In this paper, we propose a credit scoring method which combines the non-kernel fuzzy 2-norm quadratic surface SVM model, T-test feature weighting strategy and fuzzy within-class scatter together. It is worth pointing out that this new method not only saves computational time by avoiding choosing a kernel and corresponding parameters in the classical SVM models, but also addresses the "curse of dimensionality" issue and improves the robustness. Besides, we develop an efficient way to calculate the fuzzy membership of each training point by solving a linear programming problem. Finally, we conduct several numerical tests on two benchmark data sets of personal credit and one real-world data set of corporation credit. The numerical results strongly demonstrate that the proposed method outperforms eight state-of-the-art and commonly-used credit scoring methods in terms of accuracy and robustness.

中文翻译：

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通过模糊的非内核SVM和模糊的类内分散度来进行公司和个人信用评分

如今，有效的信用评分已成为在客户和企业中获得信贷市场竞争优势的非常关键的因素。本文提出了一种将非核模糊2-范数二次曲面SVM模型，T检验特征加权策略和模糊类内散布相结合的信用评分方法。值得指出的是，这种新方法不仅避免了在经典SVM模型中避免选择内核和相应参数，从而节省了计算时间，而且解决了“维数诅咒”问题，并提高了鲁棒性。此外，我们通过解决线性规划问题，开发了一种有效的方法来计算每个训练点的模糊隶属度。最后，我们对两个个人信用基准数据集和一个企业信用现实数据集进行了几个数值测试。数值结果充分证明，该方法在准确性和鲁棒性方面优于八种最新的和常用的信用评分方法。