通过集体学习方法增强区块链交易分类
Amrutanshu Panigrahi1, Abhilash Pati1, Bibhuprasad Sahu2
1Department of CSE, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India.
Scientific reports
|July 2, 2025
概括
本研究引入了一种机器学习模型,以将区块链交易分类为有风险或无风险的. 基于整体的方法实现了高精度,增强了对区块链系统的信任.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 网络安全 网络安全
背景情况:
- 区块链技术为金融,SCM和物联网提供安全的信息共享.
- 越来越多的用户数量需要强大的方法来识别恶意区块链交易.
- 保持对区块链生态系统的信任需要准确的交易风险评估.
研究的目的:
- 开发和评估一种机器学习 (ML) 模型,用于将区块链交易分类为有风险或无风险.
- 探索集合特征选择和分类方法对此任务的有效性.
主要方法:
- 采用了四种特征选择技术:CFS,RFE,RF和IG.
- 利用集体特征选择 (等级平均和等级聚合) 来结合特征子集.
- 应用集体分类 (硬投票,软投票,加权平均) 来基于学习者预测.
- 在三个不同的区块链交易数据集上评估了模型.
主要成果:
- 排名平均整体特征选择达到99.24%的最大精度.
- 排名聚合组合特征选择达到98.73%的最大精度.
- 提出的基于集合的模型在对交易风险进行分类方面表现出了很高的表现.
结论:
- 合并方法,特别是排名平均值,对于区块链交易风险分类中的特征选择非常有效.
- 开发的ML模型显著提高了识别风险交易的能力,增强了对区块链网络的信任.
- 这项研究提供了一个可扩展和准确的解决方案,用于保护区块链生态系统免受恶意活动的影响.
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