论文标题
集体数据欺诈的统计检测
Statistical Detection of Collective Data Fraud
论文作者
论文摘要
统计差异广泛应用于多媒体处理，基本上是由于数据中显示的规律性和可解释的特征。但是，在更广泛的数据领域中，这些优势可能不再可行，因此需要更通用的方法。在数据检测中，统计差异可以用作基于集体特征的相似性测量。在本文中，我们提出了一种基于统计差异的集体检测技术。该技术在数据收集之间提取分布相似性，然后使用统计差异来检测集体异常。评估表明它适用于现实世界。
Statistical divergence is widely applied in multimedia processing, basically due to regularity and interpretable features displayed in data. However, in a broader range of data realm, these advantages may no longer be feasible, and therefore a more general approach is required. In data detection, statistical divergence can be used as a similarity measurement based on collective features. In this paper, we present a collective detection technique based on statistical divergence. The technique extracts distribution similarities among data collections, and then uses the statistical divergence to detect collective anomalies. Evaluation shows that it is applicable in the real world.
