Journal of Guangxi Normal University(Natural Science Edition) ›› 2019, Vol. 37 ›› Issue (1): 71-79.doi: 10.16088/j.issn.1001-6600.2019.01.008

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Multi-label Classification Based on the Deep Autoencoder

NIE Yu1,2,3*, LIAO Xiangwen1,2,3*, WEI Jingjing4, YANG Dingda1,2,3, CHEN Guolong1,2,3   

  1. 1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou Fujian 350116, China;
    2. Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University), Fuzhou Fujian 350116, China;
    3. Digital Fujian Institute of Financial Big Data, Fuzhou University, Fuzhou Fujian 350116, China;
    4. College of Electronics and Information Science, Fujian Jiangxia University, Fuzhou Fujian 350108, China
  • Received:2018-09-25 Published:2019-01-08

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Abstract: For the issue of multi-label classification, most existing methods only take the neighborhood information into consideration and ignore the structural similarity, leading to the low accuracy of classification. Therefore, a deep autoencoder for multi-label classification is proposed in this paper. In order to capture the global network structure, this method uses orbit counting algorithm (Orca) to calculate structural similarity of each node, which is the input information of the representations in the latent space. Then, the highly-nonlinear network structure can be well preserved by jointly optimizing the global structure and the neighborhood structure in the proposed model. Finally, SVM is used to classify the nodes according to the nodes vectors obtained from the latent space. Three real-world networks are used to conduct the experiment and the results show that the new model outperforms the state-of-the-art methods in multi-label classification.

Key words: multi-label classification, network embedding, structural similarity, deep autoencoder

CLC Number: 

  • TP391
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