期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2013
卷号：4
期号：4
页码：125-128
语种：English
出版社：Ayushmaan Technologies
摘要：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 frauddetection moderation systems are commonly applied in practice to detect and prevent such illegal and fraud activities. Machinelearned 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 humantuned rule-based system.
