On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization
ACM Transactions on Mathematical Software (TOMS)
Bayesian multiple instance learning: automatic feature selection and inductive transfer
Proceedings of the 25th international conference on Machine learning
Online modeling of proactive moderation system for auction fraud detection
Proceedings of the 21st international conference on World Wide Web
Expert Systems with Applications: An International Journal
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Online auction and shopping are gaining popularity with the growth of web-based eCommerce. Criminals are also taking advantage of these opportunities to conduct fraudulent activities against honest parties with the purpose of deception and illegal profit. In practice, proactive moderation systems are deployed to detect suspicious events for further inspection by human experts. Motivated by real-world applications in commercial auction sites in Asia, we develop various advanced machine learning techniques in the proactive moderation system. Our proposed system is formulated as optimizing bounded generalized linear models in multi-instance learning problems, with intrinsic bias in selective labeling and massive unlabeled samples. In both offline evaluations and online bucket tests, the proposed system significantly outperforms the rule-based system on various metrics, including area under ROC (AUC), loss rate of labeled frauds and customer complaints. We also show that the metrics of loss rates are more effective than AUC in our cases.