A comparison of rough sets and recursive partitioning induction approaches : an application to commercial loans
(2002) International Transactions in Operational Research — Vol. 39577, p. 681-694 (2002)
Files
No attached file found for this publication.
Details
- Authors
Meskens, Nadine 
- Abstract
- (en) Credit scoring is the term used to describe methods utilised for classifying applicants for credit into classes of risk. This paper evaluates two induction approaches, rough sets and decision trees, as techniques for classifying credit (business) applicants. Inductive learning methods, like rough sets and decision trees, have better knowledge representational structure than neural networks or statistical procedures because they can be used to derive production rules. If decision tree have already been used for credit granting, the rough sets approach is rarely utilised in this domain. In this paper, we use production rules obtained on a sample of 1102 business loans in order to compare the classification abilities of the two techniques. It results that decision tree obtains better results with 87.5% of good classifications with a pruned tree against 76.7% for rough sets. However, decision tree make more type II errors than rough sets but less type I errors.
- Affiliations
Louvain School of Management Accounting & Finance
Citations
Daubie, M., Meskens, N., & Levecq, P. (2002). A comparison of rough sets and recursive partitioning induction approaches : an application to commercial loans. International Transactions in Operational Research, 39577, 681-694. https://doi.org/10.1111/1475-3995.00381 (Original work published 2002)