利用探索性数据分析通过监督学习进行欺诈诱导

Vinicius Almendra, B. Roman
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引用次数: 5

摘要

异常值检测是许多领域的相关问题,其中包括欺诈行为的检测。探索性数据分析技术对于通过可视化表示突出显示数据中的模式和偏差非常有用。较少探索的是使用它们来构建异常值检测的学习模型的可行性,该模型可以在没有人为干预的情况下自动应用于数据分类。在本文中,我们提出了一种方法,使用一种探索性数据分析技术——安德鲁斯曲线——来产生一个分类器,我们将其应用于从在线拍卖网站提取的真实数据集,在引发欺诈行为方面获得了积极的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Exploratory Data Analysis for Fraud Elicitation through Supervised Learning
Outlier detection is a relevant problem for many domains, among which for detection of fraudulent behavior. Exploratory Data Analysis techniques are known to be very useful for highlighting patterns and deviations in data through visual representations. Less explored is the feasibility of using them to build learning models for outlier detection, which can be applied automatically to classify data without human intervention. In this paper we propose a method that uses one Exploratory Data Analysis technique -- Andrews curves -- in order to produce a classifier, which we applied to a real dataset, extracted from an online auction site, obtaining positive results regarding elicitation of fraudulent behavior.
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