Effects of label noise on the classification of outlier observations

• URL: http://arxiv.org/abs/2511.08808v1
• Date: Thu, 13 Nov 2025 01:09:33 GMT
• Title: Effects of label noise on the classification of outlier observations
• Authors: Matheus Vinícius Barreto de Farias, Mario de Castro,
• Abstract summary: The study employs both synthetic and real datasets to evaluate the prediction abstention rate for outlier observations.<n>The results indicate that the addition of noise, even in small amounts, can have a significant effect on model performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study investigates the impact of adding noise to the training set classes in classification tasks using the BCOPS algorithm (Balanced and Conformal Optimized Prediction Sets), proposed by Guan & Tibshirani (2022). The BCOPS algorithm is an application of conformal prediction combined with a machine learning method to construct prediction sets such that the probability of the true class being included in the prediction set for a test observation meets a specified coverage guarantee. An observation is considered an outlier if its true class is not present in the training set. The study employs both synthetic and real datasets and conducts experiments to evaluate the prediction abstention rate for outlier observations and the model's robustness in this previously untested scenario. The results indicate that the addition of noise, even in small amounts, can have a significant effect on model performance.

Related papers

• The Lie of the Average: How Class Incremental Learning Evaluation Deceives You? [48.83567710215299]
  Class Incremental Learning (CIL) requires models to continuously learn new classes without forgetting previously learned ones.<n>We argue that a robust CIL evaluation protocol should accurately characterize and estimate the entire performance distribution.<n>We propose EDGE, an evaluation protocol that adaptively identifies and samples extreme class sequences using inter-task similarity.
  arXiv  Detail & Related papers  (2025-09-26T17:00:15Z)
• Robust Partial-Label Learning by Leveraging Class Activation Values [0.0]
  Real-world training data is often noisy; for example, human annotators assign conflicting class labels to the same instances.<n>We propose a novel method based on subjective logic, which explicitly represents uncertainty by leveraging the magnitudes of the underlying neural network's class activation values.<n>We empirically show that our method yields more robust predictions in terms of predictive performance under high noise levels.
  arXiv  Detail & Related papers  (2025-02-17T12:30:05Z)
• Conformal Prediction of Classifiers with Many Classes based on Noisy Labels [22.841631892273547]
  Conformal Prediction (CP) controls the prediction uncertainty of classification systems.<n>We show how we can estimate the noise-free conformal threshold based on the noisy labeled data.<n>We dub our approach Noise-Aware Conformal Prediction (NACP)
  arXiv  Detail & Related papers  (2025-01-22T09:35:58Z)
• A Conformal Prediction Score that is Robust to Label Noise [13.22445242068721]
  We introduce a conformal score that is robust to label noise. The noise-free conformal score is estimated using the noisy labeled data and the noise level. We show that our method outperforms current methods by a large margin, in terms of the average size of the prediction set.
  arXiv  Detail & Related papers  (2024-05-04T12:22:02Z)
• Impact of Noisy Supervision in Foundation Model Learning [91.56591923244943]
  This paper is the first work to comprehensively understand and analyze the nature of noise in pre-training datasets.<n>We propose a tuning method (NMTune) to affine the feature space to mitigate the malignant effect of noise and improve generalization.
  arXiv  Detail & Related papers  (2024-03-11T16:22:41Z)
• Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks [91.15120211190519]
  This paper aims to understand the nature of noise in pre-training datasets and to mitigate its impact on downstream tasks. We propose a light-weight black-box tuning method (NMTune) to affine the feature space to mitigate the malignant effect of noise.
  arXiv  Detail & Related papers  (2023-09-29T06:18:15Z)
• Self-Certifying Classification by Linearized Deep Assignment [65.0100925582087]
  We propose a novel class of deep predictors for classifying metric data on graphs within PAC-Bayes risk certification paradigm. Building on the recent PAC-Bayes literature and data-dependent priors, this approach enables learning posterior distributions on the hypothesis space.
  arXiv  Detail & Related papers  (2022-01-26T19:59:14Z)
• Self-supervised Pretraining with Classification Labels for Temporal Activity Detection [54.366236719520565]
  Temporal Activity Detection aims to predict activity classes per frame. Due to the expensive frame-level annotations required for detection, the scale of detection datasets is limited. This work proposes a novel self-supervised pretraining method for detection leveraging classification labels.
  arXiv  Detail & Related papers  (2021-11-26T18:59:28Z)
• Prototypical Classifier for Robust Class-Imbalanced Learning [64.96088324684683]
  We propose textitPrototypical, which does not require fitting additional parameters given the embedding network. Prototypical produces balanced and comparable predictions for all classes even though the training set is class-imbalanced. We test our method on CIFAR-10LT, CIFAR-100LT and Webvision datasets, observing that Prototypical obtains substaintial improvements compared with state of the arts.
  arXiv  Detail & Related papers  (2021-10-22T01:55:01Z)
• Training conformal predictors [0.0]
  Efficiency criteria for conformal prediction, such as emphobserved fuzziness, are commonly used to emphevaluate the performance of given conformal predictors. Here, we investigate whether it is possible to exploit such criteria to emphlearn classifiers.
  arXiv  Detail & Related papers  (2020-05-14T14:47:30Z)

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.