An insight into the effects of class imbalance and sampling on classification accuracy in credit risk assessment

Computer Science and Information Systems

Andrić, Kristina¹ 

关键词: credit risk assessment;    imbalanced data sets;    class distribution;    classification algorithms;    sample size;    undersampling;   

DOI  :  10.2298/CSIS180110037A

学科分类：社会科学、人文和艺术（综合）

来源: Computer Science and Information Systems

【 摘 要 】

In this paper we investigate the role of sample size and class distribution in credit risk assessments, focusing on real life imbalanced data sets. Choosing the optimal sample is of utmost importance for the quality of predictive models and has become an increasingly important topic with the recent advances in automating lending decision processes and the ever growing richness in data collected by financial institutions. To address the observed research gap, a large-scale experimental evaluation of real-life data sets of different characteristics was performed, using several classification algorithms and performance measures. Results indicate that various factors play a role in determining the optimal class distribution, namely the performance measure, classification algorithm and data set characteristics. The study also provides valuable insight on how to design the training sample to maximize prediction performance and the suitability of using different classification algorithms by assessing their sensitivity to class imbalance and sample size.

【 授权许可】

CC BY-NC-ND