Standard binary classifiers that maximize the overall accuracy fail in fraud detection where very few fraud cases are concealed within a large number of normal ones, as the best accuracy is often achieved by ignoring all fraud cases. In such a scenario, a natural alternative is what we refer to as a fraud-detection support vector machine, which never fails to detect fraud during training. In this article, we propose the L1-penalized fraud-detection SVM that is capable of efficiently detecting fraud cases and selecting informative variables simultaneously. We establish the piecewise linearity of the L1-penalized fraud detection SVM as a function of the regularization parameter and then develop an efficient algorithm for computing its entire regularization paths, greatly facilitating its tuning. The advantages of the L1-penalized fraud detection SVM and its path algorithm for fraud detection are numerically demonstrated using both simulated and real data sets.
Bibliographical noteFunding Information:
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MIST), Grant number 2018R1D1A1B07043034 and 2022M3J6A1063595.
© 2023, Korean Statistical Society.
- Entire regularization paths
- Fraud detection
- L-penalized SVM
- Variable selection
ASJC Scopus subject areas
- Statistics and Probability