广西师范大学学报(自然科学版) ›› 2019, Vol. 37 ›› Issue (1): 71-79.doi: 10.16088/j.issn.1001-6600.2019.01.008

• 第二十四届全国信息检索学术会议专栏 • 上一篇    下一篇

基于深度自动编码器的多标签分类研究

聂煜1,2,3, 廖祥文1,2,3*, 魏晶晶4, 杨定达1,2,3, 陈国龙1,2,3   

  1. 1.福州大学数学与计算机科学学院,福建福州350116;
    2.福建省网络计算与智能信息处理重点实验室(福州大学),福建福州350116;
    3.数字福建金融大数据研究所(福州大学), 福建福州350116;
    4.福建江夏学院电子信息科学学院,福建福州350108
  • 收稿日期:2018-09-25 发布日期:2019-01-08
  • 通讯作者: 廖祥文(1980—),男,福建泉州人,福州大学副教授,博士。E-mail:liaoxw@fzu.edu.cn
  • 基金资助:
    国家自然科学基金(61772135,U1605251);中国科学院网络数据科学与技术重点实验室开放基金 (CASNDST201708,CASNDST201606);北邮可信分布式计算与服务教育部重点实验室主任基金(2017KF01);福建省自然科学基金(2017J01755)

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

摘要: 在多标签分类的相关研究中,由于现有的基于网络表示学习算法的相关方法只利用了网络中节点之间的邻接领域信息,而没有考虑到节点之间的结构相似性,从而导致分类准确性较低,因此,本文提出一种基于深度自动编码器的多标签分类模型。该方法首先利用轨迹计算算法(Orca)计算不同规模下网络中节点的结构相似性,作为深度自动编码器的输入来改进隐藏层中的向量表示,保留网络的全局结构;然后利用节点的邻接领域信息在模型中进行联合优化,从而能有效地捕捉到网络的高度非线性结构;最后根据隐藏层得到节点的向量表示,利用支持向量机对节点进行多标签分类。验证实验采用3个公开的网络数据集,实验结果表明,与基准方法相比,本文方法在多标签分类任务中能取得更好的效果。

关键词: 多标签分类, 网络表示学习, 结构相似性, 深度自动编码器

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

中图分类号: 

  • TP391
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