콘텐츠 본문
논문 해외 국제전문학술지(SCI급) Classification Cost: An Empirical Comparison Among Traditional Classifier, Cost-Sensitive Classifier, and MetaCost
- 학술지 구분 국제전문학술지(SCI급)
- 게재년월 2012
- 저자명 Jungeun Kim, Keunho Choi, Gunwoo Kim, and Yongmoo Suh
- 학술지명 EXPERT SYSTEMS WITH APPLICATIONS
- 발행처명 PERGAMON-ELSEVIER SCIENCE LTD
- 발행국가 해외
- 논문언어 외국어
- 전체저자수 4
논문 초록 (Abstract)
Loan fraud is a critical factor in the insolvency of financial institutions, so companies make an effort to reduce the loss from fraud by building a model for proactive fraud prediction. However, there are still two critical problems to be resolved for the fraud detection: (1) the lack of cost sensitivity between type I error and type II error in most prediction models, and (2) highly skewed distribution of class in the dataset used for fraud detection because of sparse fraud-related data. The objective of this paper is to examine whether classification cost is affected both by the cost-sensitive approach and by skewed distribution of class. To that end, we compare the classification cost incurred by a traditional cost-insensitive classification approach and two cost-sensitive classification approaches, Cost-Sensitive Classifier (CSC) and MetaCost. Experiments were conducted with a credit loan dataset from a major financial institution in Korea, while varying the distribution of class in the dataset and the number of input variables. The experiments showed that the lowest classification cost was incurred when the MetaCost approach was used and when non-fraud data and fraud data were balanced. In addition, the dataset that includes all delinquency variables was shown to be most effective on reducing the classification cost. (C) 2011 Elsevier Ltd. All rights reserved.
