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Building hierarchical class structures for extreme multi-class learning

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Abstract

Class hierarchical structures play a significant role in large and complex tasks of machine learning. Existing studies on the construction of such structures follow a two-stage strategy. The category similarities are first computed with a certain assumption, and the group partition algorithm is then performed with some hyper-parameters to control the shape of class hierarchy. Despite their effectiveness in many cases, these methods suffer from two problems: (1) optimizing the two-stage objective to obtain the structure is sub-optimal; (2) hyper-parameters make the search space too large to find the optimal structure efficiently. In this paper, we propose a unified and dynamic framework to address these problems, which can: (1) jointly optimize the category similarity and group partition; (2) obtain the class hierarchical structure dynamically without any hyper-parameters. The framework replaces the traditional category similarity with the sample similarity, and constrains samples from the same atomic category partitioned to the same super-category. We theoretically prove that, within our framework, the sample similarity is equivalent to the category similarity and can balance the partitions in terms of the number of samples. Further, we design a modularity-based partition optimization algorithm that can automatically determine the number of partitions on each level. Extensive experimental results on multiple image classification datasets show that the hierarchical structure constructed by the proposed method achieves better accuracy and efficiency compared to existing methods. Additionally, the hierarchy obtained by the proposed method can benefit long-tail learning scenarios due to the balanced partition on samples.

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Data availability

All data used in this paper is publicly available and can be found in the cited paper.

Code availability

Code is available  at https://github.com/wangyuTJU/greedyIsolation.

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Acknowledgements

This work was supported in part by the National Natural Scientific Foundation of China (NSFC) under Grants 62106174, and 61732011, and in part by the China Postdoctoral Science Foundation under Grants 2021TQ0242 and 2021M690118.

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Authors and Affiliations

  1. College of Intelligence and Computing, Tian** University, Yaguan Road, Tian**, 300350, China

    Hongzhi Huang, Yu Wang & Qinghua Hu

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  1. Hongzhi Huang
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  2. Yu Wang
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  3. Qinghua Hu
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Correspondence to Yu Wang.

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Huang, H., Wang, Y. & Hu, Q. Building hierarchical class structures for extreme multi-class learning. Int. J. Mach. Learn. & Cyber. 14, 2575–2590 (2023). https://doi.org/10.1007/s13042-023-01783-z

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