【Author】
Monamo, Patrick M.; Marivate, Vukosi; Twala, Bhekisipho
【Source】2016 15TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2016)
【Abstract】In the Bitcoin network, lack of class labels tend to cause obscurities in anomalous financial behaviour interpretation. To understand fraud in the latest development of the financial sector, a multifaceted approach is proposed. In this paper, Bitcoin fraud is described from both global and local perspectives using trimmed k-means and kd-trees. The two spheres are investigated further through random forests, maximum likelihood-based and boosted binary regression models. Although both angles show good performance, global outlier perspective outperforms the local viewpoint with exception of random forest that exhibits nearby perfect results from both dimensions. This signifies that features extracted for this study describe the network fairly.
【Keywords】kd-trees; anomaly; outlier; data mining; random forest; regression
【标题】比特币欺诈检测的多层面方法:全球和本地异常值
【摘要】在比特币网络中，缺乏类别标签往往会导致异常金融行为解释的模糊性。为了理解金融部门最新发展中的欺诈行为，提出了一种多方面的方法。本文使用裁剪后的k-means和k- tree从全局和局部两个角度描述比特币欺诈。这两个领域通过随机森林，最大似然基础和推进二进制回归模型进一步调查。虽然两个角度都表现出良好的性能，但全局离群点视角的性能优于局部视角，随机森林除外，随机森林在两个维度上都表现出了近乎完美的结果。这说明本研究提取的特征对网络的描述是公平的。
【关键词】kd-trees;异常;离群值;数据挖掘;随机森林;回归
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