COMPARATIVE ANALYSIS OF CLASSIFIER FAMILIES FOR INTENT-BASED 5G NETWORK SLICING

Abstract

This study investigates the classification of 5G network slice types using the public ”Network Slicing in 5G”dataset. Weaddressacritical scientific pitfall by identifying and mitigating evaluation leakage caused by near-deterministic rule-encoded binary indicators and heavy data duplication. By excluding these artificial context flags and utilizing only a minimal set of QoS-observable telemetry — specifically packet delay, packet loss rate, time, and equipment category — we establish a rigorous, leakage-aware evaluation protocol. Five representative classifier families were evaluated using a group-safe splitting strategy to ensure results reflect real-world operational conditions. Our experimental results demonstrate that tree-based ensembles significantly outperform linear models, with the strongest ensembles reaching about 95% accuracy (Histogram-based Gradient Boosting at 94.74% and Extra Trees marginally higher at 95.14%). This research underscores that while measurable network telemetry provides sufficient signal for high-accuracy slice recognition, non linear models are necessary to navigate the complex, stochastic overlaps inherent in real wireless environments.