关键词: 信用评分, 单类分类, 不平衡数据, 高维混合数据, 混合数据主成分分析

Abstract:

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.

Key words: credit score, one-class classification, imbalance data, high-dimensional mixed data, Principal Component Analysis of Mixed Data（PCAmix）