Concept Drift Detection in Streaming Bangla Text Using an Ensemble Statistical Framework

1. We propose an ensemble that combines a BanglaBERT-plus-autoencoder reconstruction-error signal with a label-distribution signal through weighted voting over three statistical detectors.
2. We provide a controlled benchmark against six established detectors (ADWIN, DDM, EDDM, HDDM-W, Page-Hinkley, and KSWIN), reporting F1-Score and false-alarm rate on each drift type.
3. Through a per-detector analysis, we characterise which signals contribute to detection, showing that the label-distribution channel carries most of the discriminative power in our dataset.