Reliability-Aware Hybrid-K Ensemble Selection Proposed for Cervical Cytology Classification

A new arXiv preprint introduces a hybrid ensemble selection framework for multiclass cervical cytology image classification that weighs discriminative performance alongside calibration and selective prediction. The authors argue that raw accuracy is not enough for clinical image analysis, and that the method is meant to deliver trustworthy confidence scores and uncertainty flags so users know when to distrust a prediction. The work appears in the cs.AI category as an early-stage research contribution.