Multi-Label Bayesian Active Learning with Inter-Label Relationships

• URL: http://arxiv.org/abs/2411.17941v1
• Date: Tue, 26 Nov 2024 23:28:54 GMT
• Title: Multi-Label Bayesian Active Learning with Inter-Label Relationships
• Authors: Yuanyuan Qi, Jueqing Lu, Xiaohao Yang, Joanne Enticott, Lan Du,
• Abstract summary: We propose a new multi-label active learning strategy to address both challenges. Our method incorporates progressively updated positive and negative correlation matrices to capture co-occurrence and disjoint relationships. Our strategy consistently achieves more reliable and superior performance, compared to several established methods.
• Score: 3.88369051454137
• License:
• Abstract: The primary challenge of multi-label active learning, differing it from multi-class active learning, lies in assessing the informativeness of an indefinite number of labels while also accounting for the inherited label correlation. Existing studies either require substantial computational resources to leverage correlations or fail to fully explore label dependencies. Additionally, real-world scenarios often require addressing intrinsic biases stemming from imbalanced data distributions. In this paper, we propose a new multi-label active learning strategy to address both challenges. Our method incorporates progressively updated positive and negative correlation matrices to capture co-occurrence and disjoint relationships within the label space of annotated samples, enabling a holistic assessment of uncertainty rather than treating labels as isolated elements. Furthermore, alongside diversity, our model employs ensemble pseudo labeling and beta scoring rules to address data imbalances. Extensive experiments on four realistic datasets demonstrate that our strategy consistently achieves more reliable and superior performance, compared to several established methods.

Related papers

• Multi-Label Contrastive Learning : A Comprehensive Study [48.81069245141415]
  Multi-label classification has emerged as a key area in both research and industry. Applying contrastive learning to multi-label classification presents unique challenges. We conduct an in-depth study of contrastive learning loss for multi-label classification across diverse settings.
  arXiv  Detail & Related papers  (2024-11-27T20:20:06Z)
• Similarity-Dissimilarity Loss with Supervised Contrastive Learning for Multi-label Classification [11.499489446062054]
  We propose a Similarity-Dissimilarity Loss with contrastive learning for multi-label classification. Our proposed loss effectively improves the performance on all encoders under supervised contrastive learning paradigm.
  arXiv  Detail & Related papers  (2024-10-17T11:12:55Z)
• Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical [66.57396042747706]
  Complementary-label learning is a weakly supervised learning problem. We propose a consistent approach that does not rely on the uniform distribution assumption. We find that complementary-label learning can be expressed as a set of negative-unlabeled binary classification problems.
  arXiv  Detail & Related papers  (2023-11-27T02:59:17Z)
• Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning [97.88458953075205]
  Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled data. This paper proposes a novel solution called Class-Aware Pseudo-Labeling (CAP) that performs pseudo-labeling in a class-aware manner.
  arXiv  Detail & Related papers  (2023-05-04T12:52:18Z)
• One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement [71.9401831465908]
  We investigate single-positive multi-label learning (SPMLL) where each example is annotated with only one relevant label. A novel method named proposed, i.e., Single-positive MultI-label learning with Label Enhancement, is proposed. Experiments on benchmark datasets validate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-06-01T14:26:30Z)
• Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces [64.23172847182109]
  We show that different negative sampling schemes implicitly trade-off performance on dominant versus rare labels. We provide a unified means to explicitly tackle both sampling bias, arising from working with a subset of all labels, and labeling bias, which is inherent to the data due to label imbalance.
  arXiv  Detail & Related papers  (2021-05-12T15:40:13Z)
• Active Learning under Label Shift [80.65643075952639]
  We introduce a "medial distribution" to incorporate a tradeoff between importance and class-balanced sampling. We prove sample complexity and generalization guarantees for Mediated Active Learning under Label Shift (MALLS) We empirically demonstrate MALLS scales to high-dimensional datasets and can reduce the sample complexity of active learning by 60% in deep active learning tasks.
  arXiv  Detail & Related papers  (2020-07-16T17:30:02Z)
• Multi-Label Sampling based on Local Label Imbalance [7.355362369511579]
  Class imbalance is an inherent characteristic of multi-label data that hinders most multi-label learning methods. Existing multi-label sampling approaches alleviate the global imbalance of multi-label datasets. It is actually the imbalance level within the local neighbourhood of minority class examples that plays a key role in performance degradation.
  arXiv  Detail & Related papers  (2020-05-07T04:14:23Z)

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.