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LSTMDD:一个基于LSTM的优化漂移探测器,用于动态云计算中的概念漂移
Tajwar Mehmood1, Seemab Latif1, Nor Shahida Mohd Jamail2
1School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan.
PeerJ. Computer science
|March 4, 2024
概括
本研究介绍了LSTM漂移检测器 (LSTMDD),用于云计算中早期概念漂移检测. 通过超越云环境中的其他方法,LSTMDD提高了资源利用率.
科学领域:
- 云计算 云计算 云计算
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 概念漂移在云计算中带来了挑战,影响了资源利用.
- 早期发现概念漂移对于保持最佳性能至关重要.
- 现有的漂移检测方法可能不适合云环境.
研究的目的:
- 调查云计算中的概念漂移.
- 为早期概念漂移检测提出一个有效的解决方案.
- 通过改进漂移检测来提高资源利用率.
主要方法:
- 利用合成和现实世界的云数据集.
- 开发了一种经过修改的长短期内存 (LSTM) 网络,称为LSTM漂移检测器 (LSTMDD).
- 将LSTMDD与使用预测错误的现有漂移检测技术进行了比较.
主要成果:
- 在检测逐渐和突然的概念漂移方面,LSTMDD表现出卓越的性能.
- 拟议的方法优化为非高斯分布式云环境.
- LSTMDD显示出更好的异常检测能力.
结论:
- LSTM漂移检测器 (LSTMDD) 是一种有前途的机器学习方法,用于云计算中的概念漂移.
- 有效的概念漂移检测导致更有效的资源配置.
- 这些发现支持在云数据分析中使用量身定制的机器学习技术.
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