Mo-SML-02 - Progressive Domain Adaptation under Sensor Drift for Gas Classification with Limited Calibration Labels

Metal-oxide electronic noses suffer from sensor drift, in which sensor response characteristics change over time.This creates a data distribution shift (domain gap) that severely degrades gas classification accuracy.To address this drift effect, domain adaptation techniques reduce the distribution discrepancy between the initial source domain and drift-affected target measurements.We study batch-wise adaptation across temporally ordered drift batches, where calibration labels are extremely scarce.To ensure robust adaptation under unpredictable drift patterns, we train a diverse pool of conditional domain-adversarial experts.We then fuse their predictions using a confidence-aware Expectation-Maximization procedure.This fusion mechanism acts as a smart consensus, estimating each expert's reliability using the limited calibration labels without requiring additional annotations.On the UCI drift benchmark, this budget-respecting approach consistently improves target accuracy over a tuned Drift Correction Autoencoder (DCAE) baseline, particularly on later, more severely drifted batches.