Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification

Baharav, Tavor Z.; Jiang, Daniel L.; Kolluri, Kedarnath; Sanghavi, Sujay; Dhillon, Inderjit S.

doi:10.1145/3459637.3481914

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Although the classifier based on the recall hierarchy improves the speed of classification, the accuracy slightly decreases. Furthermore, more approaches to building the hierarchical classifier are based on the features of the samples [18][19][20]. Morin et al [18] construct a binary hierarchical classifier based on prior knowledge, which is extracted from the semantic feature by WordNet.…”

Section: Related Workmentioning

confidence: 99%

A Self-Supervised Tree-Structured Framework for Fine-Grained Classification

et al. 2023

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In computer vision, fine-grained classification has become an important issue in recognizing objects with slight visual differences. Usually, it is challenging to generate good performance when solving fine-grained classification problems using traditional convolutional neural networks. To improve the accuracy and training time of convolutional neural networks in solving fine-grained classification problems, this paper proposes a tree-structured framework by eliminating the effect of differences between clusters. The contributions of the proposed method include the following three aspects: (1) a self-supervised method that automatically creates a classification tree, eliminating the need for manual labeling; (2) a machine-learning matcher which determines the cluster to which an item belongs, minimizing the impact of inter-cluster variations on classification; and (3) a pruning criterion which filters the tree-structured classifier, retaining only the models with superior classification performance. The experimental evaluation of the proposed tree-structured framework demonstrates its effectiveness in reducing training time and improving the accuracy of fine-grained classification across various datasets in comparison with conventional convolutional neural network models. Specifically, for the CUB 200 2011, FGVC aircraft, and Stanford car datasets, the proposed method achieves a reduction in training time of 32.91%, 35.87%, and 14.48%, and improves the accuracy of fine-grained classification by 1.17%, 2.01%, and 0.59%, respectively.

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Section: Related Workmentioning

confidence: 99%

A Self-Supervised Tree-Structured Framework for Fine-Grained Classification

et al. 2023

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MatchXML: An Efficient Text-Label Matching Framework for Extreme Multi-Label Text Classification

Ye,

Sunderraman,

2024

IEEE Trans. Knowl. Data Eng.

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A Dual-branch Learning Model with Gradient-balanced Loss for Long-tailed Multi-label Text Classification

Yao

Zhang

et al. 2023

ACM Trans. Inf. Syst.

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Multi-label text classification has a wide range of applications in the real world. However, the data distribution in the real world is often imbalanced, which leads to serious long-tailed problems. For multi-label classification, due to the vast scale of datasets and existence of label co-occurrence, how to effectively improve the prediction accuracy of tail labels without degrading the overall precision becomes an important challenge. To address this issue, we propose A Dual-Branch Learning Model with Gradient-Balanced Loss (DBGB) based on the paradigm of existing pre-trained multi-label classification SOTA models. Our model consists of two main long-tailed module improvements. First, with the shared text representation, the dual-classifier are leveraged to process two kinds of label distributions, the one is the original data distribution and the other is the under-sampling distribution for head labels to strengthen the prediction for tail labels. Second, the proposed gradient-balanced loss can adaptively suppress the negative gradient accumulation problem related to labels, especially tail labels. We perform extensive experiments on three multi-label text classification datasets. The results show that the proposed method achieves competitive performance on overall prediction results compared to the state-of-the-art methods in solving the multi-label classification, with significant improvement on tail-label accuracy.

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Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification

Cited by 3 publications

References 17 publications

A Self-Supervised Tree-Structured Framework for Fine-Grained Classification

A Self-Supervised Tree-Structured Framework for Fine-Grained Classification

MatchXML: An Efficient Text-Label Matching Framework for Extreme Multi-Label Text Classification

A Dual-branch Learning Model with Gradient-balanced Loss for Long-tailed Multi-label Text Classification

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