Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data

• URL: http://arxiv.org/abs/2506.05721v1
• Date: Fri, 06 Jun 2025 03:59:11 GMT
• Title: Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data
• Authors: Dumindu Tissera, Omar Awadallah, Muhammad Umair Danish, Ayan Sadhu, Katarina Grolinger,
• Abstract summary: Multi-label Classification (MLC) assigns an instance to one or more non-exclusive classes.<n>It is common in MLC applications such as industrial defect detection, agricultural disease identification, and healthcare diagnosis to encounter large amounts of negative data.<n>We introduce a regularization parameter that controls the relative contribution of the absent class probabilities to the any-class presence likelihood in positive instances.
• Score: 0.6990493129893112
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-label Classification (MLC) assigns an instance to one or more non-exclusive classes. A challenge arises when the dataset contains a large proportion of instances with no assigned class, referred to as negative data, which can overwhelm the learning process and hinder the accurate identification and classification of positive instances. Nevertheless, it is common in MLC applications such as industrial defect detection, agricultural disease identification, and healthcare diagnosis to encounter large amounts of negative data. Assigning a separate negative class to these instances further complicates the learning objective and introduces unnecessary redundancies. To address this challenge, we redesign standard MLC loss functions by deriving a likelihood of any class being present, formulated by a normalized weighted geometric mean of the predicted class probabilities. We introduce a regularization parameter that controls the relative contribution of the absent class probabilities to the any-class presence likelihood in positive instances. The any-class presence likelihood complements the multi-label learning by encouraging the network to become more aware of implicit positive instances and improve the label classification within those positive instances. Experiments on large-scale datasets with negative data: SewerML, modified COCO, and ChestX-ray14, across various networks and base loss functions show that our loss functions consistently improve MLC performance of their standard loss counterparts, achieving gains of up to 6.01 percentage points in F1, 8.06 in F2, and 3.11 in mean average precision, all without additional parameters or computational complexity. Code available at: https://github.com/ML-for-Sensor-Data-Western/gmean-mlc

Related papers

• Safe Semi-Supervised Contrastive Learning Using In-Distribution Data as Positive Examples [3.4546761246181696]
  We propose a self-supervised contrastive learning approach to fully exploit a large amount of unlabeled data. The results show that self-supervised contrastive learning significantly improves classification accuracy.
  arXiv  Detail & Related papers  (2024-08-03T22:33:13Z)
• Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection [64.4350027428928]
  We propose a novel uncertainty-guided class imbalance learning framework for imbalanced social event detection tasks. Our model significantly improves social event representation and classification tasks in almost all classes, especially those uncertain ones.
  arXiv  Detail & Related papers  (2023-10-30T03:32:04Z)
• Learning in Imperfect Environment: Multi-Label Classification with Long-Tailed Distribution and Partial Labels [53.68653940062605]
  We introduce a novel task, Partial labeling and Long-Tailed Multi-Label Classification (PLT-MLC) We find that most LT-MLC and PL-MLC approaches fail to solve the degradation-MLC. We propose an end-to-end learning framework: textbfCOrrection $rightarrow$ textbfModificattextbfIon $rightarrow$ balantextbfCe.
  arXiv  Detail & Related papers  (2023-04-20T20:05:08Z)
• Parametric Classification for Generalized Category Discovery: A Baseline Study [70.73212959385387]
  Generalized Category Discovery (GCD) aims to discover novel categories in unlabelled datasets using knowledge learned from labelled samples. We investigate the failure of parametric classifiers, verify the effectiveness of previous design choices when high-quality supervision is available, and identify unreliable pseudo-labels as a key problem. We propose a simple yet effective parametric classification method that benefits from entropy regularisation, achieves state-of-the-art performance on multiple GCD benchmarks and shows strong robustness to unknown class numbers.
  arXiv  Detail & Related papers  (2022-11-21T18:47:11Z)
• Complementary Labels Learning with Augmented Classes [22.460256396941528]
  Complementary Labels Learning (CLL) arises in many real-world tasks such as private questions classification and online learning. We propose a novel problem setting called Complementary Labels Learning with Augmented Classes (CLLAC) By using unlabeled data, we propose an unbiased estimator of classification risk for CLLAC, which is guaranteed to be provably consistent.
  arXiv  Detail & Related papers  (2022-11-19T13:55:27Z)
• Analysis of Estimating the Bayes Rule for Gaussian Mixture Models with a Specified Missing-Data Mechanism [0.0]
  Semi-supervised learning (SSL) approaches have been successfully applied in a wide range of engineering and scientific fields. This paper investigates the generative model framework with a missingness mechanism for unclassified observations.
  arXiv  Detail & Related papers  (2022-10-25T06:10:45Z)
• Class-Imbalanced Complementary-Label Learning via Weighted Loss [8.934943507699131]
  Complementary-label learning (CLL) is widely used in weakly supervised classification. It faces a significant challenge in real-world datasets when confronted with class-imbalanced training samples. We propose a novel problem setting that enables learning from class-imbalanced complementary labels for multi-class classification.
  arXiv  Detail & Related papers  (2022-09-28T16:02:42Z)
• Learning from Multiple Unlabeled Datasets with Partial Risk Regularization [80.54710259664698]
  In this paper, we aim to learn an accurate classifier without any class labels. We first derive an unbiased estimator of the classification risk that can be estimated from the given unlabeled sets. We then find that the classifier obtained as such tends to cause overfitting as its empirical risks go negative during training. Experiments demonstrate that our method effectively mitigates overfitting and outperforms state-of-the-art methods for learning from multiple unlabeled sets.
  arXiv  Detail & Related papers  (2022-07-04T16:22:44Z)
• Improving Contrastive Learning on Imbalanced Seed Data via Open-World Sampling [96.8742582581744]
  We present an open-world unlabeled data sampling framework called Model-Aware K-center (MAK) MAK follows three simple principles: tailness, proximity, and diversity. We demonstrate that MAK can consistently improve both the overall representation quality and the class balancedness of the learned features.
  arXiv  Detail & Related papers  (2021-11-01T15:09:41Z)
• sigmoidF1: A Smooth F1 Score Surrogate Loss for Multilabel Classification [42.37189502220329]
  We propose a loss function, sigmoidF1, to account for the complexity of multilabel classification evaluation. We show that sigmoidF1 outperforms other loss functions on four datasets and several metrics.
  arXiv  Detail & Related papers  (2021-08-24T08:11:33Z)
• PLM: Partial Label Masking for Imbalanced Multi-label Classification [59.68444804243782]
  Neural networks trained on real-world datasets with long-tailed label distributions are biased towards frequent classes and perform poorly on infrequent classes. We propose a method, Partial Label Masking (PLM), which utilizes this ratio during training. Our method achieves strong performance when compared to existing methods on both multi-label (MultiMNIST and MSCOCO) and single-label (imbalanced CIFAR-10 and CIFAR-100) image classification datasets.
  arXiv  Detail & Related papers  (2021-05-22T18:07:56Z)

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.