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Proceedings Paper

Drug related webpages classification using images and text information based on multi-kernel learning
Author(s): Ruiguang Hu; Liping Xiao; Wenjuan Zheng
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Paper Abstract

In this paper, multi-kernel learning(MKL) is used for drug-related webpages classification. First, body text and image-label text are extracted through HTML parsing, and valid images are chosen by the FOCARSS algorithm. Second, text based BOW model is used to generate text representation, and image-based BOW model is used to generate images representation. Last, text and images representation are fused with a few methods. Experimental results demonstrate that the classification accuracy of MKL is higher than those of all other fusion methods in decision level and feature level, and much higher than the accuracy of single-modal classification.

Paper Details

Date Published: 14 December 2015
PDF: 7 pages
Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 98130F (14 December 2015); doi: 10.1117/12.2205145
Show Author Affiliations
Ruiguang Hu, Beijing Aerospace Automatic Control Institute (China)
Liping Xiao, Beijing Aerospace Automatic Control Institute (China)
Wenjuan Zheng, Beijing Aerospace Automatic Control Institute (China)


Published in SPIE Proceedings Vol. 9813:
MIPPR 2015: Pattern Recognition and Computer Vision
Tianxu Zhang; Jianguo Liu, Editor(s)

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