Active learning: theory and applications
Active learning: theory and applications
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Active learning with statistical models
Journal of Artificial Intelligence Research
Error rates for multivariate outlier detection
Computational Statistics & Data Analysis
Learning to rank for why-question answering
Information Retrieval
Online modeling of proactive moderation system for auction fraud detection
Proceedings of the 21st international conference on World Wide Web
Language Resources and Evaluation
SVM-based feature selection to optimize sensitivity-specificity balance applied to weaning
Computers in Biology and Medicine
| Hi-index | 0.00 |
In binary classification problems it is common for the two classes to be imbalanced: one case is very rare compared to the other. In this paper we consider the infinitely imbalanced case where one class has a finite sample size and the other class's sample size grows without bound. For logistic regression, the infinitely imbalanced case often has a useful solution. Under mild conditions, the intercept diverges as expected, but the rest of the coefficient vector approaches a non trivial and useful limit. That limit can be expressed in terms of exponential tilting and is the minimum of a convex objective function. The limiting form of logistic regression suggests a computational shortcut for fraud detection problems.