This paper proposes a classification model for predicting the main activity of bitcoin addresses based on their balances. Since the balances are functions of time, we apply methods from functional data analysis; more specifically, the features of the proposed classification model are the functional principal components of the data. Classifying bitcoin addresses is a relevant problem for two main reasons: to understand the composition of the bitcoin market, and to identify addresses used for illicit activities. Although other bitcoin classifiers have been proposed, they focus primarily on network analysis rather than curve behavior. Our approach, on the other hand, does not require any network information for prediction. Furthermore, functional features have the advantage of being straightforward to build, unlike expert-built features. Results show improvement when combining functional features with scalar features, and similar accuracy for the models using those features separately, which points to the functional model being a good alternative when domain-specific knowledge is not available.
翻译:本文根据比特币地址的平衡情况,提出了一个预测比特币地址主要活动的分类模式。由于这些平衡是时间功能,我们采用功能数据分析的方法;更具体地说,拟议的分类模式的特征是数据的主要功能组成部分。比特币地址的分类是一个相关的问题,主要有两个原因:了解比特币市场的构成,并查明非法活动使用的地址。虽然提出了其他比特币分类者,但它们主要侧重于网络分析,而不是曲线行为。另一方面,我们的方法并不要求任何网络信息用于预测。此外,功能特征的优点是,与专家建立的特征不同,可以直接构建。结果显示,在将功能特征与卡路里特征相结合时会有所改进,另外使用这些特征的模型的类似准确性也有所改进,这表明,当没有特定领域知识时,功能模型是一种良好的替代方法。