From Misclassifications to Outliers: Joint Reliability Assessment in Classification

2026-03-04

AI Summary

This work addresses the common practice of treating out-of-distribution (OOD) detection and in-distribution (ID) misclassification prediction as separate tasks, despite their intrinsic connection in building reliable classifiers. To bridge this gap, the authors propose SURE+, a unified framework that jointly models both tasks through a dual-scoring mechanism. The study further introduces novel joint evaluation metrics—DS-F1 and DS-AURC—to holistically assess performance across OOD and ID failure detection. Comprehensive experiments on the OpenOOD benchmark demonstrate that SURE+ significantly outperforms conventional single-score approaches, with particularly pronounced gains in scenarios involving easy or far-OOD samples. This work thus establishes a new paradigm and benchmark for trustworthy classification by explicitly integrating OOD detection and ID error prediction into a cohesive framework.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Building reliable classifiers is a fundamental challenge for deploying machine learning in real-world applications. A reliable system should not only detect out-of-distribution (OOD) inputs but also anticipate in-distribution (ID) errors by assigning low confidence to potentially misclassified samples. Yet, most prior work treats OOD detection and failure prediction as separated problems, overlooking their closed connection. We argue that reliability requires evaluating them jointly. To this end, we propose a unified evaluation framework that integrates OOD detection and failure prediction, quantified by our new metrics DS-F1 and DS-AURC, where DS denotes double scoring functions. Experiments on the OpenOOD benchmark show that double scoring functions yield classifiers that are substantially more reliable than traditional single scoring approaches. Our analysis further reveals that OOD-based approaches provide notable gains under simple or far-OOD shifts, but only marginal benefits under more challenging near-OOD conditions. Beyond evaluation, we extend the reliable classifier SURE and introduce SURE+, a new approach that significantly improves reliability across diverse scenarios. Together, our framework, metrics, and method establish a new benchmark for trustworthy classification and offer practical guidance for deploying robust models in real-world settings. The source code is publicly available at https://github.com/Intellindust-AI-Lab/SUREPlus.

Problem

• out-of-distribution detection
• failure prediction
• reliability assessment
• classification
• misclassification

Innovation

• joint reliability assessment
• double scoring functions
• OOD detection
• failure prediction
• SURE+