Credit scoring is a prominent research problem as its predictive performance is accountable for the viability of financial industry. Credit scoring datasets are high-dimensional which are related to customers’ credentials such as annual income, job status, residential status, etc. In high-dimensional data, many of the features may be irrelevant or redundant which leads to problems such as over-fitting and high computational overhead. So, effective feature selection approaches may overcome both the problems related to high-dimensional data. Generally, a features set selected by a feature selection approach is appropriate with a classifier not with all classifiers, and improves the classification performances. In this article, we have proposed a collaborative feature ranking approach with consideration of various measures which can improve the classification performance of most of the classifiers. Further, it is applied in five credit scoring datasets, results of the proposed approach are compared with various existing feature ranking approaches in terms of similarities between features selected by each pair of approaches and classification performances with respect to these sets of features.

信用评分是一个突出的研究问题，因为其预测性能对金融业的生存能力负责。信用评分数据集是高维的，与客户的年收入、工作状态、居住状况等凭证相关。在高维数据中，许多特征可能是不相关或冗余的，从而导致诸如过度使用等问题。拟合和高计算开销。因此，有效的特征选择方法可以克服与高维数据相关的两个问题。通常，通过特征选择方法选择的特征集适合一个分类器，而不是所有分类器，并提高分类性能。在本文中，我们提出了一种协同特征排序方法，考虑了各种措施，可以提高大多数分类器的分类性能。此外，它被应用于五个信用评分数据集，在每对方法选择的特征之间的相似性和关于这些特征集的分类性能方面，将所提出的方法的结果与各种现有的特征排序方法进行了比较。