Article

Learning hierarchical multi-category text classification models

Authors:

Juho Rousu,

Craig Saunders,

Sandor Szedmak,

John Shawe-TaylorAuthors Info & Claims

ICML '05: Proceedings of the 22nd international conference on Machine learning

Pages 744 - 751

https://doi.org/10.1145/1102351.1102445

Published: 07 August 2005 Publication History

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Abstract

We present a kernel-based algorithm for hierarchical text classification where the documents are allowed to belong to more than one category at a time. The classification model is a variant of the Maximum Margin Markov Network framework, where the classification hierarchy is represented as a Markov tree equipped with an exponential family defined on the edges. We present an efficient optimization algorithm based on incremental conditional gradient ascent in single-example subspaces spanned by the marginal dual variables. Experiments show that the algorithm can feasibly optimize training sets of thousands of examples and classification hierarchies consisting of hundreds of nodes. The algorithm's predictive accuracy is competitive with other recently introduced hierarchical multi-category or multilabel classification learning algorithms.

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Su HWang HLuo XXie S(2023)An end-to-end neural framework using coarse-to-fine-grained attention for overlapping relational triple extractionNatural Language Engineering10.1017/S1351324923000050(1-24)Online publication date: 21-Feb-2023
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Chen JQian Y(2022)Hierarchical Multilabel Ship Classification in Remote Sensing Images Using Label Relation GraphsIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2021.311111760(1-13)Online publication date: 2022
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Zhou XGururajan RLi YVenkataraman RTao XBargshady GBarua PKondalsamy-Chennakesavan S(2020)A survey on text classification and its applicationsWeb Intelligence10.3233/WEB-200442(1-12)Online publication date: 6-Aug-2020
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Learning hierarchical multi-category text classification models
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches

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Published In

ICML '05: Proceedings of the 22nd international conference on Machine learning

August 2005

1113 pages

ISBN:1595931805

DOI:10.1145/1102351

General Chair:
Saso Dzeroski
Jozef Stefan Institute, Slovenia
,
Program Chairs:
Luc De Raedt,
Stefan Wrobel

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Published: 07 August 2005

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Su HWang HLuo XXie S(2023)An end-to-end neural framework using coarse-to-fine-grained attention for overlapping relational triple extractionNatural Language Engineering10.1017/S1351324923000050(1-24)Online publication date: 21-Feb-2023
https://doi.org/10.1017/S1351324923000050
Chen JQian Y(2022)Hierarchical Multilabel Ship Classification in Remote Sensing Images Using Label Relation GraphsIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2021.311111760(1-13)Online publication date: 2022
https://doi.org/10.1109/TGRS.2021.3111117
Zhou XGururajan RLi YVenkataraman RTao XBargshady GBarua PKondalsamy-Chennakesavan S(2020)A survey on text classification and its applicationsWeb Intelligence10.3233/WEB-200442(1-12)Online publication date: 6-Aug-2020
https://doi.org/10.3233/WEB-200442
Huang WChen ELiu QChen YHuang ZLiu YZhao ZZhang DWang SZhu WTao DCheng XCui PRundensteiner ECarmel DHe QXu Yu J(2019)Hierarchical Multi-label Text ClassificationProceedings of the 28th ACM International Conference on Information and Knowledge Management10.1145/3357384.3357885(1051-1060)Online publication date: 3-Nov-2019
https://dl.acm.org/doi/10.1145/3357384.3357885
Defiyanti SWinarko EPriyanta S(2019)A Survey of Hierarchical Classification Algorithms with Big-Bang Approach2019 5th International Conference on Science and Technology (ICST)10.1109/ICST47872.2019.9166313(1-6)Online publication date: Jul-2019
https://doi.org/10.1109/ICST47872.2019.9166313
Liu CZhao PHuang SJiang YZhou ZMcIlraith SWeinberger K(2018)Dual set multi-label learningProceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence10.5555/3504035.3504480(3635-3642)Online publication date: 2-Feb-2018
https://dl.acm.org/doi/10.5555/3504035.3504480
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Pei YFern XRaich R(2018)Learning with Latent Label Hierarchy from Incomplete Multi-Label Data2018 24th International Conference on Pattern Recognition (ICPR)10.1109/ICPR.2018.8545329(2075-2080)Online publication date: Aug-2018
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