A Novel Outlier-Robust Accuracy Measure for Machine Learning Regression Using a Non-Convex Distance Metric
Regression, a supervised machine learning approach, establishes relationships between independent variables and a continuous dependent variable. It is widely applied in areas like price prediction and time series forecasting. The performance of regression models is typically assessed using error met...
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MDPI AG
2024-11-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/12/22/3623 |
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| author | Ahmad B. Hassanat Mohammad Khaled Alqaralleh Ahmad S. Tarawneh Khalid Almohammadi Maha Alamri Abdulkareem Alzahrani Ghada A. Altarawneh Rania Alhalaseh |
| author_facet | Ahmad B. Hassanat Mohammad Khaled Alqaralleh Ahmad S. Tarawneh Khalid Almohammadi Maha Alamri Abdulkareem Alzahrani Ghada A. Altarawneh Rania Alhalaseh |
| author_sort | Ahmad B. Hassanat |
| collection | DOAJ |
| description | Regression, a supervised machine learning approach, establishes relationships between independent variables and a continuous dependent variable. It is widely applied in areas like price prediction and time series forecasting. The performance of regression models is typically assessed using error metrics such as the Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). However, these metrics present challenges including sensitivity to outliers (notably MSE and RMSE) and scale dependency, which complicates comparisons across different models. Additionally, traditional metrics sometimes yield values that are difficult to interpret across various problems. Consequently, there is a need for a metric that consistently reflects regression model performance, independent of the problem domain, data scale, and outlier presence. To overcome these shortcomings, this paper introduces a new regression accuracy measure based on the Hassanat distance, a non-convex distance metric. This measure is not only invariant to outliers but also easy to interpret as it provides an accuracy-like value that ranges from 0 to 1 (or 0–100%). We validate the proposed metric against traditional measures across multiple benchmarks, demonstrating its robustness under various model scenarios and data types. Hence, we suggest it as a new standard for assessing regression models’ accuracy. |
| format | Article |
| id | doaj-art-c2a45180b6bf4794981a27d8bc8ad511 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-c2a45180b6bf4794981a27d8bc8ad5112024-11-26T18:12:04ZengMDPI AGMathematics2227-73902024-11-011222362310.3390/math12223623A Novel Outlier-Robust Accuracy Measure for Machine Learning Regression Using a Non-Convex Distance MetricAhmad B. Hassanat0Mohammad Khaled Alqaralleh1Ahmad S. Tarawneh2Khalid Almohammadi3Maha Alamri4Abdulkareem Alzahrani5Ghada A. Altarawneh6Rania Alhalaseh7Faculty of Information Technology, Mutah University, Karak 61710, JordanFaculty of Information Technology, Mutah University, Karak 61710, JordanFaculty of Information Technology, Mutah University, Karak 61710, JordanDepartment of Computer Science, Applied College, University of Tabuk, Tabuk 47512, Saudi ArabiaDepartment of Systems and Networking, Faculty of Computing and Information, Al-Baha University, Al-Baha 65779, Saudi ArabiaDepartment of Computer Science, Faculty of Computing and Information, Al-Baha University, Al-Baha 65779, Saudi ArabiaFaculty of Business, Mutah University, Karak 61710, JordanFaculty of Information Technology, Mutah University, Karak 61710, JordanRegression, a supervised machine learning approach, establishes relationships between independent variables and a continuous dependent variable. It is widely applied in areas like price prediction and time series forecasting. The performance of regression models is typically assessed using error metrics such as the Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). However, these metrics present challenges including sensitivity to outliers (notably MSE and RMSE) and scale dependency, which complicates comparisons across different models. Additionally, traditional metrics sometimes yield values that are difficult to interpret across various problems. Consequently, there is a need for a metric that consistently reflects regression model performance, independent of the problem domain, data scale, and outlier presence. To overcome these shortcomings, this paper introduces a new regression accuracy measure based on the Hassanat distance, a non-convex distance metric. This measure is not only invariant to outliers but also easy to interpret as it provides an accuracy-like value that ranges from 0 to 1 (or 0–100%). We validate the proposed metric against traditional measures across multiple benchmarks, demonstrating its robustness under various model scenarios and data types. Hence, we suggest it as a new standard for assessing regression models’ accuracy.https://www.mdpi.com/2227-7390/12/22/3623regressionmachine learningperformance assessmentHassanat distance |
| spellingShingle | Ahmad B. Hassanat Mohammad Khaled Alqaralleh Ahmad S. Tarawneh Khalid Almohammadi Maha Alamri Abdulkareem Alzahrani Ghada A. Altarawneh Rania Alhalaseh A Novel Outlier-Robust Accuracy Measure for Machine Learning Regression Using a Non-Convex Distance Metric Mathematics regression machine learning performance assessment Hassanat distance |
| title | A Novel Outlier-Robust Accuracy Measure for Machine Learning Regression Using a Non-Convex Distance Metric |
| title_full | A Novel Outlier-Robust Accuracy Measure for Machine Learning Regression Using a Non-Convex Distance Metric |
| title_fullStr | A Novel Outlier-Robust Accuracy Measure for Machine Learning Regression Using a Non-Convex Distance Metric |
| title_full_unstemmed | A Novel Outlier-Robust Accuracy Measure for Machine Learning Regression Using a Non-Convex Distance Metric |
| title_short | A Novel Outlier-Robust Accuracy Measure for Machine Learning Regression Using a Non-Convex Distance Metric |
| title_sort | novel outlier robust accuracy measure for machine learning regression using a non convex distance metric |
| topic | regression machine learning performance assessment Hassanat distance |
| url | https://www.mdpi.com/2227-7390/12/22/3623 |
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