面向高维混合不平衡信贷数据的单类分类方法

为实现对高维混合、不平衡信贷数据中的不良贷款者的准确预测,从降维预处理和分类算法两方面进行优化,提出一种基于混合数据主成分分析(Principal Component Analysis of Mixed Data,PCAmix)预处理的单类K近邻(K-Nearest Neighbor,KNN)计算均值算法.针对传统的主...

One-Class Classification Method for High-Dimensional Mixed and Unbalanced Credit Score Data

To conduct an accurate prediction of “bad” loan applicants in high-dimensional, mixed and unbalanced credit score data, this paper proposes a one-class [KNN][（K]-Nearest Neighbor） algorithm based on Principal Component Analysis of Mixed Data processing（PCAmix）, in which both the preprocessing of dimension reduction and classification itself are optimized. Since the traditional Principal Component Analysis（PCA） methods cannot deal with qualitative variables directly, this paper not only employs the PCAmix, but also incorporates the concept of one-class classification and average distance calculation to avoid the poor performance of binary classification on unbalanced data. Besides, the proposed method adopts the Bootstrap algorithm to find the best decision boundaries that maximize the separation of positive and negative samples to accomplish accurate predicting for customer’s default risk. The experiments on UCI datasets of German and Default credit score show that the proposed algorithm performs better when the data are high-dimensional, mixed as well as unbalanced.