Fusing Multiple Hierarchies for Semantic Hierarchical Classification - Volume 6 Number 1 (Feb. 2016) - IJMLC
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Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
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.

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