论文标题
成本敏感的欺诈应用程序的半监督分类
Cost-sensitive Semi-supervised Classification for Fraud Applications
论文作者
论文摘要
这项研究探讨了欺诈检测领域中对成本敏感的学习（CSL），以减少欺诈类别的错误预测并提高其准确性。值得注意的是，我们专注于船舶竞标欺诈，这是挑战性检测的，因为Shill和合法投标者的行为相似。我们研究了半监督分类（SSC）框架中的CSL，以解决标记的欺诈数据的稀缺性。我们的论文是将CSL与SSC集成以进行欺诈检测的首次尝试。我们采用元CSL方法来管理错误分类错误的成本，而SSC算法则使用不平衡的数据培训。使用实际的Shill竞标数据集，我们评估了几种CSL和SSC混合模型的性能，然后在统计上比较其错误分类错误和准确率。最有效的CSL+SSC模型能够检测到99％的欺诈者，总成本最低。
This research explores Cost-Sensitive Learning (CSL) in the fraud detection domain to decrease the fraud class's incorrect predictions and increase its accuracy. Notably, we concentrate on shill bidding fraud that is challenging to detect because the behavior of shill and legitimate bidders are similar. We investigate CSL within the Semi-Supervised Classification (SSC) framework to address the scarcity of labeled fraud data. Our paper is the first attempt to integrate CSL with SSC for fraud detection. We adopt a meta-CSL approach to manage the costs of misclassification errors, while SSC algorithms are trained with imbalanced data. Using an actual shill bidding dataset, we assess the performance of several hybrid models of CSL and SSC and then compare their misclassification error and accuracy rates statistically. The most efficient CSL+SSC model was able to detect 99% of fraudsters and with the lowest total cost.
