928
Views
1
CrossRef citations to date
0
Altmetric
Theory and Methods

Angle-Based Hierarchical Classification Using Exact Label Embedding

, , ORCID Icon &
Pages 704-717 | Received 05 Dec 2018, Accepted 20 Jul 2020, Published online: 16 Sep 2020

References

  • Akata, Z. , Perronnin, F. , Harchaoui, Z. , and Schmid, C. (2016), “Label-Embedding for Image Classification,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 38, 1425–1438. DOI: https://doi.org/10.1109/TPAMI.2015.2487986.
  • Akata, Z. , Reed, S. , Walter, D. , Lee, H. , and Schiele, H. (2015), “Evaluation of Output Embeddings for Fine-Grained Image Classification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR2015) , Boston, IEEE Computer Society, pp. 2927–2936.
  • Babbar, R. , Partalas, I. , Gaussier, E. , and Amini, M.-R. (2013), “On Flat Versus Hierarchical Classification in Large-Scale Taxonomies,” in Proceedings of the 26th International Conference on Neural Information Processing Systems (NIPS2013 ), Nevada, Curran Associates Inc., pp. 1824–1832.
  • Babbar, R. , Partalas, I. , Gaussier, E. , Amini, M.-R. , and Amblard, C. (2016), “Learning Taxonomy Adaptation in Large-Scale Classification,” The Journal of Machine Learning Research , 17, 3350–3386.
  • Barbedo, J. G. A. , and Lopes, A. (2006), “Automatic Genre Classification of Musical Signals,” EURASIP Journal on Advances in Signal Processing , 2007, 49–60.
  • Bartlett, P. L. , Jordan, M. I. , and McAuliffe, J. D. (2006), “Convexity, Classification, and Risk Bounds,” Journal of the American Statistical Association , 101, 138–156.
  • Bengio, S. , Weston, J. , and Grangier, D. (2010), “Label Embedding Trees for Large Multi-Class Tasks,” in Advances in Neural Information Processing Systems 23 (NIPS2010) , Vancouver, Curran Associates Inc., pp. 163–171.
  • Cai, L. , and Hofmann, T. (2004), “Hierarchical Document Categorization With Support Vector Machines,” in Proceedings of the Thirteenth ACM International Conference on Information and Knowledge Management (CIKM2004) , Washington, ACM Press, pp. 78–87. DOI: https://doi.org/10.1145/1031171.1031186.[]
  • Cesa-Bianchi, N. , Conconi, A. , and Gentile, C. (2004), “Regret Bounds for Hierarchical Classification With Linear-Threshold Functions,” in Proceedings of the 17th Annual Conference on Learning Theory (COLT2004) , Banff, Springer Berlin Heidelberg, pp. 93–108.
  • Cesa-Bianchi, N. , Gentile, C. , and Zaniboni, L. (2006), “Hierarchical Classification: Combining Bayes With SVM,” in Proceedings of the 23rd International Conference on Machine Learning, ACM, pp. 177–184.
  • Chang, C. C. , and Lin, C. J. (2011), “Libsvm: A Library for Support Vector Machines,” ACM Transactions on Intelligent Systems and Technology , 2, 1–27.
  • Charuvaka, A. , and Rangwala, H. (2015), “Hiercost: Improving large Scale Hierarchical Classification With Cost Sensitive Learning,” in Machine Learning and Knowledge Discovery in Databases , Porto, Springer International Publishing, pp. 675–690.
  • Chen, J. , and Warren, D. (2013), “Cost-Sensitive Learning for Large-Scale Hierarchical Classification,” in Proceedings of the 22nd ACM International Conference on Information and Knowledge Management (CIKM2013), San Francisco, ACM Press, pp. 1351–1360. DOI: https://doi.org/10.1145/2505515.2505582.[]
  • Chollet, F. (2016), “Information-Theoretical Label Embeddings for Large-Scale Image Classification,” arXiv no. 1607.05691.
  • Davies, M. N. , Secker, A. , Freitas, A. A. , Mendao, M. , Timmis, J. , and Flower, D. R. (2007), “On the Hierarchical Classification of G Protein-Coupled Receptors,” Bioinformatics , 23, 3113–3118. DOI: https://doi.org/10.1093/bioinformatics/btm506.
  • DeCoro, C. , Barutcuoglu, Z. , and Fiebrink, R. (2007), “Bayesian Aggregation for Hierarchical Genre Classification,” in Proceedings of the 8th International Symposium on Music Information Retrieval (ISMIR2007) , Vienna, OCG Press, pp. 77–80.
  • Fan, J. , Zhang, J. , Mei, K. , Peng, J. , and Gao, L. (2015), “Cost-Sensitive Learning of Hierarchical Tree Classifiers for Large-Scale Image Classification and Novel Category Detection,” Pattern Recognition , 48, 1673–1687.
  • Gopal, S. , and Yang, Y. (2013), “Recursive Regularization for Large-Scale Classification With Hierarchical and Graphical Dependencies,” in Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, ACM Press, pp. 257–265. DOI: https://doi.org/10.1145/2487575.2487644.[]
  • Hayete, B. , and Bienkowska, J. R. (2005), “Gotrees: Predicting Go Associations From Protein Domain Composition Using Decision Trees,” in Proceedings of the Pacific Symposium on Biocomputing , Hawaii, World Scientific, pp. 140–151.
  • Hoyoux, T. , Rodríguez-Sánchez, A. J. , and Piater, J. H. (2016), “Can Computer Vision Problems Benefit From Structured Hierarchical Classification?,” Machine Vision and Applications , 27, 1299–1312.
  • Kiritchenko, S. , Matwin, S. , and Famili, A. F. (2005), “Functional Annotation of Genes Using Hierarchical Text Categorization,” in Proceedings of the ACL Workshop on Linking Biological Literature, Ontologies and Databases: Mining Biological Semantics , Michigan.
  • Koller, D. , and Sahami, M. (1997), “Hierarchically Classifying Documents Using Very Few Words,” in Proceedings of the Fourteenth International Conference on Machine Learning (ICML1997) , Nashville, Morgan Kaufmann Publishers Inc., pp. 170–178.
  • Kosmopoulos, A. , Partalas, I. , Gaussier, E. , Paliouras, G. , and Androutsopoulos, I. (2015), “Evaluation Measures for Hierarchical Classification: A Unified View and Novel Approaches,” Data Mining and Knowledge Discovery , 29, 820–865.
  • Lange, K. , and Wu, T. T. (2008), “An MM Algorithm for Multicategory Vertex Discriminant Analysis,” Journal of Computational and Graphical Statistics , 17, 527–544.
  • Lewis, D. D. , Yang, Y. , Rose, T. G. , and Li, F. (2004), “Rcv1: A New Benchmark Collection for Text Categorization Research,” Journal of Machine Learning Research , 5, 361–397.
  • Li, R. , Zhong, W. , and Zhu, L. (2012), “Feature Screening via Distance Correlation Learning,” Journal of the American Statistical Association , 107, 1129–1139. DOI: https://doi.org/10.1080/01621459.2012.695654.
  • Liu, Y. , Zhang, H. H. , and Wu, Y. (2011), “Hard or Soft Classification? Large-Margin Unified Machines,” Journal of the American Statistical Association , 106, 166–177. DOI: https://doi.org/10.1198/jasa.2011.tm10319.
  • Shao, Y. H. , Chen, W. J. , Wang, Z. , Li, C. N. , and Deng, N. Y. (2015), “Weighted Linear Loss Twin Support Vector Machine for Large-Scale Classification,” Knowledge-Based Systems , 73, 276–288.
  • Shen, X. , and Wang, L. (2007), “Generalization Error for Multi-Class Margin Classification,” Electronic Journal of Statistics , 1, 307–330.
  • Silla, C. N. , and Freitas, A. A. (2011), “A Survey of Hierarchical Classification Across Different Application Domains,” Data Mining and Knowledge Discovery , 22, 31–72.
  • Silla, C. N., Jr. , and Freitas, A. A. (2009), “Novel Top-Down Approaches for Hierarchical Classification and Their Application to Automatic Music Genre Classification,” in Proceedings of the 2009 IEEE International Conference on Systems, Man and Cybernetics (SMC2009) , San Antonio, IEEE, pp. 182–196.
  • Tsochantaridis, I. , Joachims, T. , Hofmann, T. , and Altun, Y. (2005), “Large Margin Methods for Structured and Interdependent Output Variables,” Journal of Machine Learning Research , 6, 1453–1484.
  • Vens, C. , Struyf, J. , Schietgat, L. , Džeroski, S. , and Blockeel, H. (2008), “Decision Trees for Hierarchical Multi-Label Classification,” Machine Learning , 73, 185–214.
  • Wang, H. , Shen, X. , and Pan, W. (2011), “Large Margin Hierarchical Classification With Mutually Exclusive Class Membership,” Journal of Machine Learning Research , 12, 2721–2748.
  • Wang, J. , Shen, X. , and Pan, W. (2009), “On Large Margin Hierarchical Classification With Multiple Paths,” Journal of the American Statistical Association , 104, 1213–1223. DOI: https://doi.org/10.1198/jasa.2009.tm08084.
  • Weinberger, K. Q. , and Chapelle, O. (2009), “Large Margin Taxonomy Embedding for Document Categorization,” in Advances in Neural Information Processing Systems 21 (NIPS2009) , Vancouver, Curran Associates Inc., pp. 1737–1744.
  • Wu, T. T. , and Lange, K. (2010), “Multicategory Vertex Discriminant Analysis for High-Dimensional Data,” The Annals of Applied Statistics , 4, 1698–1721.
  • Wu, T. T. , and Wu, Y. (2012), “Nonlinear Vertex Discriminant Analysis With Reproducing Kernels,” Statistical Analysis and Data Mining , 5, 167–176. DOI: https://doi.org/10.1002/sam.11137.
  • Wu, Y. , and Liu, Y. (2013), “Adaptively Weighted Large Margin Classifiers,” Journal of Computational and Graphical Statistics , 22, 416–432.
  • Zhang, C. , and Liu, Y. (2014), “Multicategory Angle-Based Large-Margin Classification,” Biometrika , 101, 625–640. DOI: https://doi.org/10.1093/biomet/asu017.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.