document: Online modeling of proactive moderation system for auction fraud detection We consider the problem of building online machine-learned models for detecting auction frauds in e-commence web sites . Since the emergence of the world wide web , online shopping and online auction have gained more and more popularity . While people are enjoying the benefits from online trading , criminals are also taking advantages to conduct fraudulent activities against honest parties to obtain illegal profit . Hence proactive fraud-detection moderation systems are commonly applied in practice to detect and prevent such illegal and fraud activities . Machine-learned models , especially those that are learned online , are able to catch frauds more efficiently and quickly than human-tuned rule-based systems . In this paper , we propose an online probit model framework which takes online feature selection , coefficient bounds from human knowledge and multiple instance learning into account simultaneously . By empirical experiments on a real-world online auction fraud detection data we show that this model can potentially detect more frauds and significantly reduce customer complaints compared to several baseline models and the human-tuned rule-based system .
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