Review of Extreme Multilabel Classification

• URL: http://arxiv.org/abs/2302.05971v2
• Date: Sun, 26 Mar 2023 19:39:59 GMT
• Title: Review of Extreme Multilabel Classification
• Authors: Arpan Dasgupta, Siddhant Katyan, Shrutimoy Das, Pawan Kumar
• Abstract summary: Extreme multilabel classification or XML, is an active area of interest in machine learning. The community has come up with a useful set of metrics to identify correctly the prediction for head or tail labels.
• Score: 1.888738346075831
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Extreme multilabel classification or XML, is an active area of interest in machine learning. Compared to traditional multilabel classification, here the number of labels is extremely large, hence, the name extreme multilabel classification. Using classical one versus all classification wont scale in this case due to large number of labels, same is true for any other classifiers. Embedding of labels as well as features into smaller label space is an essential first step. Moreover, other issues include existence of head and tail labels, where tail labels are labels which exist in relatively smaller number of given samples. The existence of tail labels creates issues during embedding. This area has invited application of wide range of approaches ranging from bit compression motivated from compressed sensing, tree based embeddings, deep learning based latent space embedding including using attention weights, linear algebra based embeddings such as SVD, clustering, hashing, to name a few. The community has come up with a useful set of metrics to identify correctly the prediction for head or tail labels.

Related papers

• Towards Imbalanced Large Scale Multi-label Classification with Partially Annotated Labels [8.977819892091]
  Multi-label classification is a widely encountered problem in daily life, where an instance can be associated with multiple classes. In this work, we address the issue of label imbalance and investigate how to train neural networks using partial labels.
  arXiv  Detail & Related papers  (2023-07-31T21:50:48Z)
• Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations [91.67511167969934]
  imprecise label learning (ILL) is a framework for the unification of learning with various imprecise label configurations. We demonstrate that ILL can seamlessly adapt to partial label learning, semi-supervised learning, noisy label learning, and, more importantly, a mixture of these settings.
  arXiv  Detail & Related papers  (2023-05-22T04:50:28Z)
• Bridging the Gap between Model Explanations in Partially Annotated Multi-label Classification [85.76130799062379]
  We study how false negative labels affect the model's explanation. We propose to boost the attribution scores of the model trained with partial labels to make its explanation resemble that of the model trained with full labels.
  arXiv  Detail & Related papers  (2023-04-04T14:00:59Z)
• Pairwise Instance Relation Augmentation for Long-tailed Multi-label Text Classification [38.66674700075432]
  We propose a Pairwise Instance Relation Augmentation Network (PIRAN) to augment tailed-label documents for balancing tail labels and head labels. PIRAN consistently outperforms the SOTA methods, and dramatically improves the performance of tail labels.
  arXiv  Detail & Related papers  (2022-11-19T12:45:54Z)
• An Effective Approach for Multi-label Classification with Missing Labels [8.470008570115146]
  We propose a pseudo-label based approach to reduce the cost of annotation without bringing additional complexity to the classification networks. By designing a novel loss function, we are able to relax the requirement that each instance must contain at least one positive label. We show that our method can handle the imbalance between positive labels and negative labels, while still outperforming existing missing-label learning approaches.
  arXiv  Detail & Related papers  (2022-10-24T23:13:57Z)
• Multi-label Classification with High-rank and High-order Label Correlations [62.39748565407201]
  Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix factorization. We propose a simple yet effective method to depict the high-order label correlations explicitly, and at the same time maintain the high-rank of the label matrix. Comparative studies over twelve benchmark data sets validate the effectiveness of the proposed algorithm in multi-label classification.
  arXiv  Detail & Related papers  (2022-07-09T05:15:31Z)
• Large Loss Matters in Weakly Supervised Multi-Label Classification [50.262533546999045]
  We first regard unobserved labels as negative labels, casting the W task into noisy multi-label classification. We propose novel methods for W which reject or correct the large loss samples to prevent model from memorizing the noisy label. Our methodology actually works well, validating that treating large loss properly matters in a weakly supervised multi-label classification.
  arXiv  Detail & Related papers  (2022-06-08T08:30:24Z)
• Structured Semantic Transfer for Multi-Label Recognition with Partial Labels [85.6967666661044]
  We propose a structured semantic transfer (SST) framework that enables training multi-label recognition models with partial labels. The framework consists of two complementary transfer modules that explore within-image and cross-image semantic correlations. Experiments on the Microsoft COCO, Visual Genome and Pascal VOC datasets show that the proposed SST framework obtains superior performance over current state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2021-12-21T02:15:01Z)
• A Study on the Autoregressive and non-Autoregressive Multi-label Learning [77.11075863067131]
  We propose a self-attention based variational encoder-model to extract the label-label and label-feature dependencies jointly. Our model can therefore be used to predict all labels in parallel while still including both label-label and label-feature dependencies.
  arXiv  Detail & Related papers  (2020-12-03T05:41:44Z)
• Multilabel Classification by Hierarchical Partitioning and Data-dependent Grouping [33.48217977134427]
  We exploit the sparsity of label vectors and the hierarchical structure to embed them in low-dimensional space. We present a novel data-dependent grouping approach, where we use a group construction based on a low-rank Nonnegative Matrix Factorization. We then present a hierarchical partitioning approach that exploits the label hierarchy in large scale problems to divide up the large label space and create smaller sub-problems.
  arXiv  Detail & Related papers  (2020-06-24T22:23:39Z)

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.