机器学习算法的效率分析的简化方法
Muthuramalingam Sivakumar1, Sudhaman Parthasarathy2, Thiyagarajan Padmapriya2
1Department of Computer Science and Engineering, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India.
PeerJ. Computer science
|December 9, 2024
概括
本研究介绍了一个框架,用于评估机器学习 (ML) 算法效率,使用训练时间和内存使用等指标. 该方法有助于优化特定应用的ML性能,从医学成像到作物预测.
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
- 人工智能的人工智能
背景情况:
- 机器学习 (ML) 算法效率对于资源限制或实时需求的应用程序至关重要.
- 现有的评估方法可能无法全面捕捉ML效率的多面性质.
研究的目的:
- 为评估ML算法效率提供一个全面的框架.
- 纳入关键指标,包括培训时间,预测时间,内存使用率和计算资源利用率.
主要方法:
- 一种多步骤的方法,涉及原始指标收集,规范化和分析层次过程 (AHP) 进行权重.
- 基于规范化指标和AHP衍生权重的复合效率得分的计算.
- 该框架应用于不同的数据集:医学图像数据和农作物预测数据.
主要成果:
- 该框架有效地根据特定应用需求区分了ML算法性能.
- 对于医疗图像分析,该框架突出了算法在稳定性和适应性方面的优势.
- 对于农作物预测,该框架强调了可扩展性和资源管理.
结论:
- 开发的框架提供了一种多功能工具,用于在不同领域评估和提高ML算法效率.
- 为寻求针对特定用例优化ML算法的从业者提供有价值的见解.
- 证明框架在现实场景中的适用性和有效性.
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