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Proceedings Paper

Heterogeneous features extraction based on deep learning for drug-related webpages classification
Author(s): Ruiguang Hu; Qi Gao; Yujiao Jia; Yanxin Liu
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Paper Abstract

In this paper, heterogeneous features extraction is conducted by deep learning for drug-related webpages classification. First, body text and image-label text are extracted through HTML parsing, and effective 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. Webpages representation is generated by concatenating representations of text and images. Heterogeneous feature extraction are conducted by deep learning and classical methods, such as PCA, respectively. Feature selection is also conducted using information theory. Last, extracted features and selected features are classified. Experimental results demonstrate that the classification accuracy of features extracted by deep learning is higher than those of features extracted or selected by classical methods, and also higher than the accuracy of single modal classification.

Paper Details

Date Published: 14 February 2020
PDF: 6 pages
Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 1143002 (14 February 2020); doi: 10.1117/12.2535014
Show Author Affiliations
Ruiguang Hu, Beijing Aerospace Automatic Control Institute (China)
Qi Gao, Beijing Aerospace Automatic Control Institute (China)
Yujiao Jia, Beijing Aerospace Automatic Control Institute (China)
Yanxin Liu, Beijing Aerospace Automatic Control Institute (China)


Published in SPIE Proceedings Vol. 11430:
MIPPR 2019: Pattern Recognition and Computer Vision
Nong Sang; Jayaram K. Udupa; Yuehuan Wang; Zhenbing Liu, Editor(s)

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