Metacognitive Selective Ensemble for Mobile Systems
Read the original on arXiv Machine Learning →MetaSE is an active ensemble framework that selects a small set of reliable models for mobile sensing tasks, reducing computational cost while maintaining accuracy. It leverages short-term persistence in model reliability, uses post-execution evidence to prune unreliable members, and only triggers lightweight routing when replacements are needed. Experiments on four human activity recognition datasets and four model architectures show MetaSE outperforms a fixed three-model ensemble and matches the accuracy of more expensive adaptive and full-ensemble approaches, achieving 2.7× speedup and 69% memory savings on a Raspberry Pi 4B.
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