Adaptive Model Selection using Meta Models and Drift Adaptation

Smart spaces that have deployed machine learning (ML) models for intelligent decision typically evolve over time. Model selection is a key step in deploying (ML) models, particularly in dynamic environments where data distribution shifts can impact model accuracy over time. Existing approaches often rely on evaluating many candidate models against predefined metrics, which is computationally expensive and neither suitable for real-time applications nor dynamic environments. This paper presents an adaptive model selection technique that combines meta-modeling with drift detection to improve efficiency and robustness. Meta-models are used to evaluate the suitability of candidate models under different constraints, such as predictive accuracy and computational cost, without requiring full evaluation on the target data. This reduces the overhead of model selection while preserving deployment quality. In addition, a drift detection mechanism monitors changes in the data distribution and updates the selection strategy accordingly. The proposed approach contributes to automated machine learning (AutoML) by enabling adaptive, efficient, and reliable model selection in real-time environments.