A New Approach with Convez Hull to Measure Classification Complezity of Credit Scoring Database
Credit scoring is a typical binary classification problem. Its significance to financial institutions has brought application of many quantitative methods. Most published research is focused on increasing classification performance by adjusting algorithms, generally without a corresponding analysis of intrinsic dataset difficulties. Prior research shows that these intrinsic difficulties cause all methods to yield less than perfect classification of testing samples in dataset. Hence, our discussion concentrates on the complexity of datasets. In this study, a new approach based on convex hull is suggested as a means to measure the classification complexity of credit scoring datasets. An empirical example is provided to demonstrate the efficiency of the new approach.
complezity measures credit scoring convez hull
Ligang Zhou Kin Keung Lai Jerome Yen
Department of Management Sciences City University of Hong Kong Hong Kong Department of Finance Hong Kong University of Science and Technology, Hong Kong
国际会议
北京
英文
441-444
2009-07-24（万方平台首次上网日期，不代表论文的发表时间）