For detecting malicious bidding activities in e‐auctions, this study develops a chunk‐based incremental learning framework that can operate in real‐world auction settings. The self‐adaptive framework first classifies incoming bidder chunks to counter fraud in each auction and take necessary actions. The fraud classifier is then adjusted with confident bidders' labels validated via bidder verification and one‐class classification. Based on real fraud data produced from commercial auctions, we conduct an extensive experimental study wherein the classifier is adapted incrementally using only relevant bidding data while evaluating the subsequent adjusted models' detection and misclassification rates. We also compare our classifier with static learning and learning without data relevancy.

中文翻译：

---

真实欺诈检测环境的增量学习框架

为了检测电子拍卖中的恶意竞标活动，本研究开发了一种基于块的增量学习框架，该框架可在现实世界的拍卖环境中运行。自适应框架首先对传入的投标者数据块进行分类，以应对每次拍卖中的欺诈行为并采取必要的措施。然后，对欺诈分类器进行调整，并使用经过投标人验证和一类分类验证的自信投标人标签。基于商业拍卖产生的真实欺诈数据，我们进行了广泛的实验研究，其中仅使用相关的投标数据对分类器进行增量调整，同时评估后续调整后的模型的检测和错误分类率。我们还将分类器与静态学习和没有数据相关性的学习进行比较。