加密货币交易异常检测中的主动学习

IF 4 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Leandro L. Cunha, Miguel A. Brito, Domingos F. Oliveira, Ana P. Martins
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引用次数: 0

摘要

加密货币市场已大幅增长,而这种快速增长催生了欺诈行为。有必要建立欺诈检测机制。标签不足是训练高性能有监督分类器的一个障碍,这项工作解决了这一难题。它旨在减少费力费时的人工标注。一些未标注的数据点具有更相关、信息量更大的标签,可供有监督模型学习。目前正在研究利用无监督异常检测算法和主动学习策略,在没有初始标签交易的冷启动场景中建立一个获取标签交易的迭代过程的可行性。目标是研究数据子集的异常检测能力,最大限度地发挥监督模型的学习潜力。结果显示,异常检测算法的性能良好。研究结果表明,异常检测算法需要专门用于涉及冷启动的情况。因此,使用主动学习技术将产生更好的结果和监督机器学习模型性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Active Learning in the Detection of Anomalies in Cryptocurrency Transactions
The cryptocurrency market has grown significantly, and this quick growth has given rise to scams. It is necessary to put fraud detection mechanisms in place. The challenge of inadequate labeling is addressed in this work, which is a barrier to the training of high-performance supervised classifiers. It aims to lessen the necessity for laborious and time-consuming manual labeling. Some unlabeled data points have labels that are more pertinent and informative for the supervised model to learn from. The viability of utilizing unsupervised anomaly detection algorithms and active learning strategies to build an iterative process of acquiring labeled transactions in a cold start scenario, where there are no initial-labeled transactions, is being investigated. Investigating anomaly detection capabilities for a subset of data that maximizes supervised models’ learning potential is the goal. The anomaly detection algorithms under performed, according to the results. The findings underscore the need that anomaly detection algorithms be reserved for situations involving cold starts. As a result, using active learning techniques would produce better outcomes and supervised machine learning model performance.
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来源期刊
CiteScore
6.30
自引率
0.00%
发文量
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审稿时长
7 weeks
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