Combined Multiple Machine Learning Performance Measure

The Combined Multiple Machine Learning Performance Measure (CMMLPM) offers a groundbreaking framework for evaluating machine learning (ML) systems by integrating multiple performance metrics into a single comprehensive measure. Traditional evaluation methods often treat performance metrics in isolation, complicating the assessment process when conflicting performance measures arise. CMMLPM effectively addresses this challenge by providing a unified metric that facilitates easier comparison and optimization of machine learning models across diverse applications. Through rigorous analysis and case studies, the robustness of CMMLPM in real-world scenarios have been demonstrated including credit card fraud detection and biometric authentication systems. Our findings reveal that CMMLPM not only streamlines the evaluation process but also enhances decision-making for selecting the most effective ML models. This innovative approach not only advances the field of performance measurement in machine learning but also sets the stage for future research to refine performance evaluation frameworks, ensuring they remain relevant in the increasingly complex landscape of machine learning applications.

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Publication Details

Journal
IEEE Transactions on Instrumentation and Measurement
Published
2025-01-01
DOI
https://doi.org/10.1109/tim.2025.3562989
Citations
1
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
2.28
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article

Combined Multiple Machine Learning Performance Measure

Song-Kyoo Kim
1 citations
IEEE Transactions on Instrumentation and Measurement
Big Data and Business Intelligence
2.28
article

Combined Multiple Machine Learning Performance Measure

Song-Kyoo Kim
article en
1 citations

Abstract

The Combined Multiple Machine Learning Performance Measure (CMMLPM) offers a groundbreaking framework for evaluating machine learning (ML) systems by integrating multiple performance metrics into a single comprehensive measure. Traditional evaluation methods often treat performance metrics in isolation, complicating the assessment process when conflicting performance measures arise. CMMLPM effectively addresses this challenge by providing a unified metric that facilitates easier comparison and optimization of machine learning models across diverse applications. Through rigorous analysis and case studies, the robustness of CMMLPM in real-world scenarios have been demonstrated including credit card fraud detection and biometric authentication systems. Our findings reveal that CMMLPM not only streamlines the evaluation process but also enhances decision-making for selecting the most effective ML models. This innovative approach not only advances the field of performance measurement in machine learning but also sets the stage for future research to refine performance evaluation frameworks, ensuring they remain relevant in the increasingly complex landscape of machine learning applications.

IEEE Transactions on Instrumentation and MeasurementVol. 74
Macao Polytechnic University (MO)
Openalex Percentile: Top 13%
Big Data and Business Intelligence
2.28
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Combined Multiple Machine Learning Performance Measure — Song-Kyoo Kim · IEEE Transactions on Instrumentation and Measurement (2025) | TGRS Research Map | TGRS