Decision Tree Learning Algorithms for WDM Systems' Performance Prediction Article Swipe

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Emadeldeen Morsy , Nour Eldin Ismail , Moustafa H. Aly , Karim Banawan ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.21203/rs.3.rs-4391700/v1

We consider the problem of predicting the performance of an operational wavelength division multiplexing (WDM) system using machine learning techniques. We assess the performance of the WDM system using the quality value metric (Q-val). To that end, we construct an actual WDM dataset, whose records are generated using four live WDM systems. We export these records using different optical measurement tools, such as reflectometers, spectrum analyzers, and the operating management system for each WDM system. To circumvent the difficulty of theoretical analysis of WDM system performance, we propose using machine learning techniques for predicting such performance. We employ the decision tree learning techniques to predict the Q-val, namely, the decision tree regressor (DTR) and the decision tree classifier (DTC). By training DTR on the constructed dataset and using shuffle-split cross-validation, we show that DTR outperforms DTC in terms of the R₂ score, specifically, our DTR achieves an R₂ score of 96.2%, while the testing score records 89.65%. The proposed DTR machine learning model outperforms the known benchmark of support vector machine (SVM), which merely achieves a training R₂ score of 92.2%. In addition, our numerical results show ≈ 3000 reduction in total running time when using DTR compared to SVM.

Related Topics

Decision Tree

Computer Science

Wavelength-Division Multiplexing

Decision Tree Learning

Machine Learning

Algorithm

Artificial Intelligence

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Decision tree Computer science Wavelength-division multiplexing Decision tree learning Tree (set theory) Machine learning Algorithm Artificial intelligence Mathematics Optics Combinatorics Wavelength Physics

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Type: preprint
Language: en
Landing Page: https://doi.org/10.21203/rs.3.rs-4391700/v1
PDF: https://www.researchsquare.com/article/rs-4391700/latest.pdf
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DOI: https://doi.org/10.21203/rs.3.rs-4391700/v1

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Title: Decision Tree Learning Algorithms for WDM Systems' Performance Prediction

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-05-20

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Authors: Emadeldeen Morsy, Nour Eldin Ismail, Moustafa H. Aly, Karim Banawan

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Landing page: https://doi.org/10.21203/rs.3.rs-4391700/v1

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Concepts: Decision tree, Computer science, Wavelength-division multiplexing, Decision tree learning, Tree (set theory), Machine learning, Algorithm, Artificial intelligence, Mathematics, Optics, Combinatorics, Wavelength, Physics

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