论文标题
比特币地址的功能分类
Functional Classification of Bitcoin Addresses
论文作者
论文摘要
本文提出了一个分类模型，用于根据其余额来预测比特币地址的主要活动。由于余额是时间的函数，因此我们采用功能数据分析的方法。更具体地说，提出的分类模型的特征是数据的功能主组件。分类比特币地址是一个相关问题，其主要原因是：了解比特币市场的组成，并确定用于非法活动的地址。尽管已经提出了其他比特币分类器，但它们主要关注网络分析而不是曲线行为。另一方面，我们的方法不需要任何网络信息进行预测。此外，与专家构建的功能不同，功能功能具有直接构建的优势。结果在将功能特征与标量特征相结合时显示出改进，并且使用这些功能分别使用这些功能的模型准确性，这表明功能模型是当域特异性知识不可用时的良好替代方案。
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.
