ArticleSensors (Basel, Switzerland)2026
Comparative Evaluation of Machine Learning and Hyperparameter Optimization Methods for Low-Cost CO
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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5 authors.
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Abstract
Low-cost sensors (LCSs) are increasingly used in air quality monitoring because of their affordability and scalability; however, their limited accuracy necessitates reliable calibration approaches. Although machine learning (ML)-based calibration methods have shown promising results, direct comparisons of hyperparameter optimization (HPO) strategies remain challenging due to differences in datasets, search spaces, and optimization budgets. In this study, ML models and HPO methods were evaluated within a standardized experimental framework developed on the AQ-MultiCal platform. Grid Search (GS), Random Search (RS), and Bayesian Optimization (BO) were implemented using identical hyperparameter search spaces and equal iteration budgets across both short-term and long-term real-world CO
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