חיפוש גרפי יעיל לתיקון שגיאות פוסט-הוק במחלצי קלאסיפיקציה בינארית

תקציר מקורי באנגליתarXiv:2401.04282v2 Announce Type: replace Abstract: We introduce a model-agnostic framework for constrained post-hoc error correction in binary classifiers. Given a frozen base classifier, the method searches for an interpretable conjunction of feature--threshold rules that corrects residual false-positive or false-negative errors while explicitly constraining newly introduced errors. The approach combines graph-based search over candidate rule paths, depth-dependent dynamic constraints, and a reduced-histogram procedure for efficient threshold evaluation. Unlike retraining or modifying the base classifier, the learned correction path operates on its predictions and can therefore be applied to arbitrary binary classifiers with suitable input features. Experiments on a large binary-classifi