A simplified approach for efficiency analysis of machine learning algorithms
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 me...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2024-11-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2418.pdf |
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| author | Muthuramalingam Sivakumar Sudhaman Parthasarathy Thiyagarajan Padmapriya |
| author_facet | Muthuramalingam Sivakumar Sudhaman Parthasarathy Thiyagarajan Padmapriya |
| author_sort | Muthuramalingam Sivakumar |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c6f3d8cfe0634aaf9dbfca9e60d8712c |
| institution | DOAJ |
| issn | 2376-5992 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-c6f3d8cfe0634aaf9dbfca9e60d8712c2025-08-20T02:48:42ZengPeerJ Inc.PeerJ Computer Science2376-59922024-11-0110e241810.7717/peerj-cs.2418A simplified approach for efficiency analysis of machine learning algorithmsMuthuramalingam Sivakumar0Sudhaman Parthasarathy1Thiyagarajan Padmapriya2Department of Computer Science and Engineering, Thiagarajar College of Engineering, Madurai, Tamil Nadu, IndiaDepartment of Applied Mathematics and Computational Science, Thiagarajar College of Engineering, Madurai, Tamil Nadu, IndiaDepartment of Applied Mathematics and Computational Science, Thiagarajar College of Engineering, Madurai, Tamil Nadu, IndiaThe 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.https://peerj.com/articles/cs-2418.pdfMachine learning efficiencyComposite efficiency scoreAnalytic Hierarchy Process (AHP)Metric normalizationAlgorithm performance evaluationMedical image analysis |
| spellingShingle | Muthuramalingam Sivakumar Sudhaman Parthasarathy Thiyagarajan Padmapriya A simplified approach for efficiency analysis of machine learning algorithms PeerJ Computer Science Machine learning efficiency Composite efficiency score Analytic Hierarchy Process (AHP) Metric normalization Algorithm performance evaluation Medical image analysis |
| title | A simplified approach for efficiency analysis of machine learning algorithms |
| title_full | A simplified approach for efficiency analysis of machine learning algorithms |
| title_fullStr | A simplified approach for efficiency analysis of machine learning algorithms |
| title_full_unstemmed | A simplified approach for efficiency analysis of machine learning algorithms |
| title_short | A simplified approach for efficiency analysis of machine learning algorithms |
| title_sort | simplified approach for efficiency analysis of machine learning algorithms |
| topic | Machine learning efficiency Composite efficiency score Analytic Hierarchy Process (AHP) Metric normalization Algorithm performance evaluation Medical image analysis |
| url | https://peerj.com/articles/cs-2418.pdf |
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