计算机工程与应用 ›› 2016, Vol. 52 ›› Issue (14): 156-160.

• 模式识别与人工智能 • 上一篇    下一篇

一种获取标签相关信息的多标签分类方法

郑伦川1,邓亚平2   

  1. 1.重庆科创职业学院 信息工程学院,重庆 402160
    2.重庆邮电大学 计算机科学与技术学院,重庆 400065
  • 出版日期:2016-07-15 发布日期:2016-07-18

Novel multi-label classification method by acquiring label relevant information

ZHENG Lunchuan1, DENG Yaping2   

  1. 1.College of Information Engineering, Chongqing Creation Vocational College, Chongqing 402160, China
    2.College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
  • Online:2016-07-15 Published:2016-07-18

摘要: 多标签分类已在很多领域得到了实际的应用。针对多标签分类中存在标签相关性问题,提出一种获取标签相关信息的多标签分类新方法,记为LRI_MLC。该方法主要是通过引入一个概率模型来实现,即对建立的最优化子问题采用交替最大化法进行求解,并给出了求解推到过程,自动地获得标签的相关信息,以达到较好的多标签分类效果。在四个多标签数据集上的实验结果表明,提出的方法得到了较好的分类预测评价值以及其他几种衡量指标值,优于现有经典的多标签分类方法。

关键词: 多标签分类, 标签关联信息, 概率模型, 交替最大化算法

Abstract: Multi-label classification methods have been applied in many real-world fields. To the problem of label-relevance in multi-label classification, this paper proposes a novel multi-label classification method by automatically acquiring label relevant information. The method introduces a probabilistic model to solve the established optimization sub-problem by using alternative maximization algorithm. It also gives the inference. This model can acquire label relevant information, and it can achieve better multi-label classification results. The experimental results on four real multi-label datasets show that the proposed method can achieve higher classification and prediction evaluation values and several other measure index values than the existing multi-label classification methods.

Key words: multi-label classification, label relevant information, probabilistic model, alternative maximization algorithm