Balancing accuracy, complexity and interpretability in consumer credit decision making: A C-TOPSIS classification approach

Authors:
Xiaoqian Zhu;Jianping Li;Dengsheng Wu;Haiyan Wang;Changzhi Liang
Affiliations:
Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, China and University of Chinese Academy of Sciences, Beijing 100190, China;Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, China;Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, China;Jiangsu Province Institute of Quality & Safety Engineering, Nanjing 210046, China;Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, China and University of Chinese Academy of Sciences, Beijing 100190, China
Venue:
Knowledge-Based Systems
Year:
2013

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Abstract

Accuracy, complexity and interpretability are very important in credit classification. However, most approaches cannot perform well in all the three aspects simultaneously. The objective of this study is to put forward a classification approach named C-TOPSIS that can balance the three aspects well. C-TOPSIS is based on the rationale of TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). TOPSIS is famous for reliable evaluation results and quick computing process and it is easy to understand and use. However, it is a ranking approach and three challenges have to be faced for modifying TOPSIS into a classification approach. C-TOPSIS works out three strategies to overcome the challenges and retains the advantages of TOPSIS. So C-TOPSIS is deduced to have reliable classification results, high computational efficiency and ease of use and understanding. Our findings in the experiment verify the advantages of C-TOPSIS. In comparison with 7 popular approaches on 2 widely used UCI credit datasets, C-TOPSIS ranks 2nd in accuracy, 1st in complexity and is in 1st rank in interpretability. Only C-TOPSIS ranks among the top 3 in all the three aspects, which verifies that C-TOPSIS can balance accuracy, complexity and interpretability well.