Home > Archive > 2016 > Volume 6 Number 1 (Feb. 2016) >
IJMLC 2016 Vol.6(1): 47-51 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2016.6.1.570

Fusing Multiple Hierarchies for Semantic Hierarchical Classification

Shuo Zhao and Quan Zou

Abstract—This paper studies the problem of constructing a suitable hierarchy for hierarchical classification. It presents a new method to fuse multiple similarity relatedness between concepts. The method is based on the kernel target alignment technology. We also develop a method to construct a hierarchy for image classification automatically. The hierarchy is constructed based on the previous fused similarity measure. Then, we utilize the structured support vector machine (SVM) for classification with a meaningful hierarchy. Experiments on tow real-world datasets show that hierarchical classification perform better than flat classification, and the structured SVM with the fused classes hierarchy provides a better image classification.

Index Terms—Hierarchies construction, hierarchical classification, taxonomies, structural learning.

The authors are with School of Computer Science and Technology, Tianjin University, Tianjin 300350, P. R.China (e-mail: szhao@tju.edu.cn, zouquan@nclab.net).

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Cite: Shuo Zhao and Quan Zou, "Fusing Multiple Hierarchies for Semantic Hierarchical Classification," International Journal of Machine Learning and Computing vol.6, no. 1, pp. 47-51, 2016.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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