Sivakumar Muthuramalingam, Parthasarathy Sudhaman, Padmapriya Thiyagarajan
Department of Computer Science and Engineering, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India.
Department of Applied Mathematics and Computational Science, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India.
PeerJ Comput Sci. 2024 Nov 28;10:e2418. doi: 10.7717/peerj-cs.2418. eCollection 2024.
The efficiency of machine learning (ML) algorithms plays a critical role in their deployment across various applications, particularly those with resource constraints or real-time requirements. This article presents a comprehensive framework for evaluating ML algorithm efficiency by incorporating metrics, such as training time, prediction time, memory usage, and computational resource utilization. The proposed methodology involves a multistep process: collecting raw metrics, normalizing them, applying the Analytic Hierarchy Process (AHP) to determine weights, and computing a composite efficiency score. We applied this framework to two distinct datasets: medical image data and agricultural crop prediction data. The results demonstrate that our approach effectively differentiates algorithm performance based on the specific demands of each application. For medical image analysis, the framework highlights strengths in robustness and adaptability, whereas for agricultural crop prediction, it emphasizes scalability and resource management. This study provides valuable insights into optimizing ML algorithms, and offers a versatile tool for practitioners to assess and enhance algorithmic efficiency across diverse domains.
机器学习（ML）算法的效率在其跨各种应用的部署中起着关键作用，特别是在那些有资源限制或实时要求的应用中。本文提出了一个综合框架，通过纳入训练时间、预测时间、内存使用和计算资源利用率等指标来评估ML算法的效率。所提出的方法涉及一个多步骤过程：收集原始指标、对其进行归一化、应用层次分析法（AHP）确定权重以及计算综合效率得分。我们将这个框架应用于两个不同的数据集：医学图像数据和农作物预测数据。结果表明，我们的方法能够根据每个应用的特定需求有效地区分算法性能。对于医学图像分析，该框架突出了鲁棒性和适应性方面的优势，而对于农作物预测，它强调了可扩展性和资源管理。这项研究为优化ML算法提供了有价值的见解，并为从业者提供了一个通用工具，用于评估和提高跨不同领域的算法效率。
