Fine-Tuning Network Slicing in 5G: Unveiling Mathematical Equations for Precision Classification

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@Article{andelic:2025:Computers,

• author = "Nikola Andelic and Sandi {Baressi Segota} and Vedran Mrzljak",
• title = "Fine-Tuning Network Slicing in {5G:} Unveiling Mathematical Equations for Precision Classification",
• journal = "Computers",
• year = "2025",
• volume = "14",
• number = "5",
• pages = "Article No. 159",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2073-431X",
• URL = "https://www.mdpi.com/2073-431X/14/5/159",
• DOI = "doi:10.3390/computers14050159",
• abstract = "Modern 5G network slicing centers on the precise design of virtual, independent networks operating over a shared physical infrastructure, each configured to meet specific service requirements. This approach plays a vital role in enabling highly customized and flexible service delivery within the 5G ecosystem. In this study, we present the application of a genetic programming symbolic classifier to a dedicated network slicing dataset, resulting in the generation of accurate symbolic expressions for classifying different network slice types. To address the issue of class imbalance, we employ oversampling strategies that produce balanced variations of the dataset. Furthermore, a random search strategy is used to explore the hyperparameter space comprehensively in pursuit of optimal classification performance. The derived symbolic models, refined through threshold tuning based on prediction correctness, are subsequently evaluated on the original imbalanced dataset. The proposed method demonstrates outstanding performance, achieving a perfect classification accuracy of 1.0.",
• notes = "also known as \cite{computers14050159}",

}

Genetic Programming entries for Nikola Andelic Sandi Baressi Segota Vedran Mrzljak

Citations