MetaModelSelect：轻量级事后置信度元模型用于选择性下一位置预测

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Abstract:Next-location prediction can support proactive mobile-network decisions, but acting on unreliable predictions can waste resources. We apply MetaModelSelect, a lightweight post-hoc confidence metamodel, to selective cellular next-location prediction. The mobility-specific model combines a learned embedding of the predicted location with the four largest softmax probabilities and Softmax Concentration. It learns whether a frozen recurrent neural network's top-1 prediction is correct, using only its outputs and without retraining the backbone. On path-balanced Changchun cellular mobility trajectories, we evaluate backbones trained with cross-entropy (CE) and self-adaptive training plus entropy minimization (SAT+EM), comparing against eight scalar confidence scores, two location-aware lookup methods, and a raw-logit multilayer perceptron. Relative to maximum softmax probability, MetaModelSelect achieves mean per-run reductions in area under the risk--coverage curve (AURC), excess AURC (E-AURC), and area under the generalized risk--coverage curve (AUGRC) of 2.6%, 4.2%, and 3.0% for CE, and 2.0%, 3.3%, and 1.9% for SAT+EM. It also achieves the lowest reported mean risk at every evaluated partial coverage. The results show that MetaModelSelect improves confidence ranking in this mobility setting while leaving the underlying location predictor unchanged.

Comments:	accepted at IEEE International Conference on Tools with Artificial Intelligence (ICTAI) 2026
Subjects:	Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
Cite as:	arXiv:2610.19394 [cs.LG]
	(or arXiv:2610.19394v1 [cs.LG] for this version)
	https://doi.org/10.48550/arXiv.2610.19394 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Calvin Jary PhD [view email]
[v1] Wed, 14 Oct 2026 01:39:56 UTC (5,825 KB)