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A simplified approach for efficiency analysis of machine learning algorithms.

Muthuramalingam Sivakumar1, Sudhaman Parthasarathy2, Thiyagarajan Padmapriya2

  • 1Department of Computer Science and Engineering, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India.

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Summary

This study introduces a framework to evaluate machine learning (ML) algorithm efficiency using metrics like training time and memory usage. The method aids in optimizing ML performance for specific applications, from medical imaging to crop prediction.

Keywords:
Agricultural data predictionAlgorithm performance evaluationAnalytic Hierarchy Process (AHP)Composite efficiency scoreMachine learning efficiencyMedical image analysisMetric normalization

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Area of Science:

  • Computer Science
  • Data Science
  • Artificial Intelligence

Background:

  • Machine learning (ML) algorithm efficiency is crucial for applications with resource constraints or real-time needs.
  • Existing evaluation methods may not comprehensively capture the multifaceted nature of ML efficiency.

Purpose of the Study:

  • To present a comprehensive framework for evaluating ML algorithm efficiency.
  • To incorporate key metrics including training time, prediction time, memory usage, and computational resource utilization.

Main Methods:

  • A multistep methodology involving raw metric collection, normalization, and the Analytic Hierarchy Process (AHP) for weighting.
  • Computation of a composite efficiency score based on normalized metrics and AHP-derived weights.
  • Application of the framework to distinct datasets: medical image data and agricultural crop prediction data.

Main Results:

  • The framework effectively differentiates ML algorithm performance based on application-specific demands.
  • For medical image analysis, the framework highlighted algorithm strengths in robustness and adaptability.
  • For agricultural crop prediction, the framework emphasized scalability and resource management.

Conclusions:

  • The developed framework offers a versatile tool for assessing and enhancing ML algorithm efficiency across diverse domains.
  • Provides valuable insights for practitioners seeking to optimize ML algorithms for specific use cases.
  • Demonstrates the framework's applicability and effectiveness in real-world scenarios.