基于标签关联的多标签演化超网络

doi:10.3969/j.issn.0372-2112.2018.04.032

电子学报 ›› 2018, Vol. 46 ›› Issue (4): 1012-1018.DOI: 10.3969/j.issn.0372-2112.2018.04.032

基于标签关联的多标签演化超网络

王进, 刘彬, 孙开伟, 陈乔松, 邓欣

重庆邮电大学计算智能重庆市重点实验室, 重庆 400065

收稿日期:2016-05-12 修回日期:2017-07-14 出版日期:2018-04-25
- 作者简介:
- 王进男,1979年1月出生于重庆,工学博士,2008年在韩国仁荷大学获得工学博士学位,现为重庆邮电大学教授.主要研究方向为数据挖掘、机器学习、智能交通.E-mail:wangjin@cqupt.edu.cn;刘彬男,1989年11月出生于河北保定,2016年在重庆邮电大学获得工学硕士学位,现为重庆邮电大学助教.主要研究方向为数据挖掘
- 基金资助:
- 重庆市基础与前沿研究计划 (No.cstc2014jcyjA40001，No.cstc2014jcyjA40022）; 重庆教委科学技术研究项目 (自然科学类） (No.KJ1400436）

Multi-Label Evolutionary Hypernetwork Based on Label Correlations

WANG Jin, LIU Bin, SUN Kai-wei, CHEN Qiao-song, DENG Xin

Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Received:2016-05-12 Revised:2017-07-14 Online:2018-04-25 Published:2018-04-25
- Supported by:
- Chongqing Research Program of Basic and Frontier Technology (No.cstc2014jcyjA40001, No.cstc2014jcyjA40022); Science and Technology Research Program of Chongqing Municipal Education Commission (Natural Science) (No.KJ1400436)

摘要/Abstract

摘要： 针对多标签学习中如何有效挖掘利用高阶标签关联的问题，提出了一种基于标签关联的多标签演化超网络模型.该模型通过输入任意多标签学习方法的预测结果，利用超边表征挖掘高阶标签关联，并综合标签关联和特征信息作为最终的预测结果.与3种传统多标签学习方法在6个多标签数据集上的对比实验表明，本文提出模型不仅能够有效提升多个传统多标签学习方法的性能，而且能够提供具有良好可读性的学习结果.

关键词: 机器学习, 多标签学习, 演化超网络, 标签关联

Abstract:

In order to solve the problem that how to explore and exploit the high-order label correlations effectively in multi-label learning, a Multi-Label evolutionary HyperNetwork based on label Correlations (MLHNC) is proposed in this paper. In MLHNC, the predicting results obtained from any multi-label learning method are utilized as input of the model, the high-order correlations among labels are represented and explored by hyperedges, and the final prediction is made by integrating the label correlation and feature information. The experimental results on six multi-label datasets compared with three state-of-the-art multi-label learning methods show that the MLHNC not only improves the performance of various state-of-the-art multi-label learning methods, but also provides readable learning results.

Key words: machine learning, multi-label learning, evolutionary hypernetwork, label correlation

中图分类号:

TP39

王进, 刘彬, 孙开伟, 等. 基于标签关联的多标签演化超网络[J]. 电子学报, 2018, 46(4): 1012-1018.

WANG Jin, LIU Bin, SUN Kai-wei, et al. Multi-Label Evolutionary Hypernetwork Based on Label Correlations[J]. Acta Electronica Sinica, 2018, 46(4): 1012-1018.

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基于标签关联的多标签演化超网络

Multi-Label Evolutionary Hypernetwork Based on Label Correlations

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