Balancing Prediction Accuracy and Explanation Power of Path Loss Modeling in a University Campus Environment via Explainable AI
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| Publié dans: | Future Internet vol. 17, no. 4 (2025), p. 155 |
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| Autres auteurs: | , |
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MDPI AG
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| Accès en ligne: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3194606931 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1999-5903 | ||
| 024 | 7 | |a 10.3390/fi17040155 |2 doi | |
| 035 | |a 3194606931 | ||
| 045 | 2 | |b d20250401 |b d20250430 | |
| 084 | |a 231464 |2 nlm | ||
| 100 | 1 | |a Khalili Hamed |u Research Group Computer Networks, Faculty of Computer Science, University of Koblenz, D-56070 Koblenz, Germany; frey@uni-koblenz.de | |
| 245 | 1 | |a Balancing Prediction Accuracy and Explanation Power of Path Loss Modeling in a University Campus Environment via Explainable AI | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a For efficient radio network planning, empirical path loss (PL) prediction models are utilized to predict signal attenuation in different environments. Alternatively, machine learning (ML) models are proposed to predict path loss. While empirical models are transparent and require less computational capacity, their predictions are not able to generate accurate forecasting in complex environments. While ML models are precise and can cope with complex terrains, their opaque nature hampers building trust and relying assertively on their predictions. To fill the gap between transparency and accuracy, in this paper, we utilize glass box ML using Microsoft research’s explainable boosting machines (EBM) together with the PL data measured for a university campus environment. Moreover, polar coordinate transformation is applied in our paper, which unravels the superior explanation capacity of the feature transmitting angle beyond the feature distance. PL predictions of glass box ML are compared with predictions of black box ML models as well as those generated by empirical models. The glass box EBM exhibits the highest performance. The glass box ML, furthermore, sheds light on the important explanatory features and the magnitude of their effects on signal attenuation in the underlying propagation environment. | |
| 610 | 4 | |a Covenant University | |
| 653 | |a Accuracy | ||
| 653 | |a Machine learning | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Attenuation | ||
| 653 | |a Communication | ||
| 653 | |a Prediction models | ||
| 653 | |a College campuses | ||
| 653 | |a Coordinate transformations | ||
| 653 | |a Polar coordinates | ||
| 653 | |a Literature reviews | ||
| 653 | |a Explainable artificial intelligence | ||
| 653 | |a Colleges & universities | ||
| 700 | 1 | |a Frey, Hannes |u Research Group Computer Networks, Faculty of Computer Science, University of Koblenz, D-56070 Koblenz, Germany; frey@uni-koblenz.de | |
| 700 | 1 | |a Wimmer, Maria A |u Research Group E-Government, Faculty of Computer Science, University of Koblenz, D-56070 Koblenz, Germany; wimmer@uni-koblenz.de | |
| 773 | 0 | |t Future Internet |g vol. 17, no. 4 (2025), p. 155 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3194606931/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3194606931/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3194606931/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |