Fragility-aware Classification for Understanding Risk and Improving Generalization

• URL: http://arxiv.org/abs/2502.13024v1
• Date: Tue, 18 Feb 2025 16:44:03 GMT
• Title: Fragility-aware Classification for Understanding Risk and Improving Generalization
• Authors: Chen Yang, Zheng Cui, Daniel Zhuoyu Long, Jin Qi, Ruohan Zhan,
• Abstract summary: We introduce the Fragility Index (FI), a novel metric that evaluates classification performance from a risk-averse perspective. We derive exact reformulations for cross-entropy loss, hinge-type loss, and Lipschitz loss, and extend the approach to deep learning models.
• Score: 6.926253982569273
• License:
• Abstract: Classification models play a critical role in data-driven decision-making applications such as medical diagnosis, user profiling, recommendation systems, and default detection. Traditional performance metrics, such as accuracy, focus on overall error rates but fail to account for the confidence of incorrect predictions, thereby overlooking the risk of confident misjudgments. This risk is particularly significant in cost-sensitive and safety-critical domains like medical diagnosis and autonomous driving, where overconfident false predictions may cause severe consequences. To address this issue, we introduce the Fragility Index (FI), a novel metric that evaluates classification performance from a risk-averse perspective by explicitly capturing the tail risk of confident misjudgments. To enhance generalizability, we define FI within the robust satisficing (RS) framework, incorporating data uncertainty. We further develop a model training approach that optimizes FI while maintaining tractability for common loss functions. Specifically, we derive exact reformulations for cross-entropy loss, hinge-type loss, and Lipschitz loss, and extend the approach to deep learning models. Through synthetic experiments and real-world medical diagnosis tasks, we demonstrate that FI effectively identifies misjudgment risk and FI-based training improves model robustness and generalizability. Finally, we extend our framework to deep neural network training, further validating its effectiveness in enhancing deep learning models.

Related papers

• Quantifying calibration error in modern neural networks through evidence based theory [0.0]
  This paper introduces a novel framework for quantifying the trustworthiness of neural networks by incorporating subjective logic into the evaluation of Expected Error (ECE) We demonstrate the effectiveness of this approach through experiments on MNIST and CIFAR-10 datasets where post-calibration results indicate improved trustworthiness. The proposed framework offers a more interpretable and nuanced assessment of AI models, with potential applications in sensitive domains such as healthcare and autonomous systems.
  arXiv  Detail & Related papers  (2024-10-31T23:54:21Z)
• Deep Evidential Learning for Radiotherapy Dose Prediction [0.0]
  We present a novel application of an uncertainty-quantification framework called Deep Evidential Learning in the domain of radiotherapy dose prediction. We found that this model can be effectively harnessed to yield uncertainty estimates that inherited correlations with prediction errors upon completion of network training.
  arXiv  Detail & Related papers  (2024-04-26T02:43:45Z)
• Analyzing Adversarial Inputs in Deep Reinforcement Learning [53.3760591018817]
  We present a comprehensive analysis of the characterization of adversarial inputs, through the lens of formal verification. We introduce a novel metric, the Adversarial Rate, to classify models based on their susceptibility to such perturbations. Our analysis empirically demonstrates how adversarial inputs can affect the safety of a given DRL system with respect to such perturbations.
  arXiv  Detail & Related papers  (2024-02-07T21:58:40Z)
• Inadequacy of common stochastic neural networks for reliable clinical decision support [0.4262974002462632]
  Widespread adoption of AI for medical decision making is still hindered due to ethical and safety-related concerns. Common deep learning approaches, however, have the tendency towards overconfidence under data shift. This study investigates their actual reliability in clinical applications.
  arXiv  Detail & Related papers  (2024-01-24T18:49:30Z)
• Uncertainty Estimation by Fisher Information-based Evidential Deep Learning [61.94125052118442]
  Uncertainty estimation is a key factor that makes deep learning reliable in practical applications. We propose a novel method, Fisher Information-based Evidential Deep Learning ($mathcalI$-EDL) In particular, we introduce Fisher Information Matrix (FIM) to measure the informativeness of evidence carried by each sample, according to which we can dynamically reweight the objective loss terms to make the network more focused on the representation learning of uncertain classes.
  arXiv  Detail & Related papers  (2023-03-03T16:12:59Z)
• Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty [52.03490691733464]
  We introduce DEviS, an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks. By leveraging subjective logic theory, we explicitly model probability and uncertainty for the problem of medical image segmentation. DeviS incorporates an uncertainty-aware filtering module, which utilizes the metric of uncertainty-calibrated error to filter reliable data.
  arXiv  Detail & Related papers  (2023-01-01T05:02:46Z)
• An Uncertainty-Informed Framework for Trustworthy Fault Diagnosis in Safety-Critical Applications [1.988145627448243]
  Low trustworthiness of deep learning-based prognostic and health management (PHM) hinders its applications in safety-critical assets. We propose an uncertainty-informed framework to diagnose faults and meanwhile detect the OOD dataset. We show that the proposed framework is of particular advantage in tackling unknowns and enhancing the trustworthiness of fault diagnosis in safety-critical applications.
  arXiv  Detail & Related papers  (2021-10-08T21:24:14Z)
• Uncertainty-Aware Training for Cardiac Resynchronisation Therapy Response Prediction [3.090173647095682]
  Quantifying uncertainty of a prediction is one way to provide such interpretability and promote trust. We quantify the data (aleatoric) and model (epistemic) uncertainty of a DL model for Cardiac Resynchronisation Therapy response prediction from cardiac magnetic resonance images. We perform a preliminary investigation of an uncertainty-aware loss function that can be used to retrain an existing DL image-based classification model to encourage confidence in correct predictions.
  arXiv  Detail & Related papers  (2021-09-22T10:37:50Z)
• Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning [123.3472310767721]
  Prediction credibility measures are fundamental in statistics and machine learning. These measures should account for the wide variety of models used in practice. The framework developed in this work expresses the credibility as a risk-fit trade-off.
  arXiv  Detail & Related papers  (2020-11-24T19:52:38Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)
• Accurate and Robust Feature Importance Estimation under Distribution Shifts [49.58991359544005]
  PRoFILE is a novel feature importance estimation method. We show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
  arXiv  Detail & Related papers  (2020-09-30T05:29:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.