一种适应于非完备标签数据和标签关联性的多标签分类方法

张丽娜; 戴灵鹏; 匡泰

doi:10.11959/j.issn.1000-0801.2016197

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一种适应于非完备标签数据和标签关联性的多标签分类方法

研究与开发 | 更新时间：2024-06-05

- 一种适应于非完备标签数据和标签关联性的多标签分类方法
- A multi-label classification method for disposing incomplete labeled data and label relevance
- 电信科学 2016年32卷第8期页码：82-89
- 作者机构：
  
  1. 浙江安防职业技术学院信息工程系，浙江温州 325016
  2. 温州大学生命与环境科学学院，浙江温州 325035
- 作者简介：
  
  [ "张丽娜（1980-），女，浙江安防职业技术学院信息工程系讲师，主要研究方向为数据挖掘、图像处理、模式识别。" ]
  [ "戴灵鹏（1975-），男，博士，温州大学生命与环境科学学院副教授，主要研究方向为模式识别。" ]
  [ "匡泰（1964-），男，浙江安防职业技术学院信息工程系副教授，主要研究方向为大数据、人工智能。" ]
- 基金信息：
  
  浙江省教育科学规划基金资助项目;Education Science Department Foundation of Zhejiang Province(2016SCG188);浙江省自然科学基金资助项目;The Natural Science Foundation of Zhejiang Province of China(LY14C03007)
- DOI：10.11959/j.issn.1000-0801.2016197
  中图分类号： TP311
- 网络出版日期：2016-08，
  
  纸质出版日期：2016-08-20
- 稿件说明：
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张丽娜, 戴灵鹏, 匡泰. 一种适应于非完备标签数据和标签关联性的多标签分类方法[J]. 电信科学, 2016,32(8):82-89.

Lina ZHANG, Lingpeng DAI, Tai KUANG. A multi-label classification method for disposing incomplete labeled data and label relevance[J]. Telecommunications science, 2016, 32(8): 82-89.
张丽娜, 戴灵鹏, 匡泰. 一种适应于非完备标签数据和标签关联性的多标签分类方法[J]. 电信科学, 2016,32(8):82-89. DOI： 10.11959/j.issn.1000-0801.2016197.

Lina ZHANG, Lingpeng DAI, Tai KUANG. A multi-label classification method for disposing incomplete labeled data and label relevance[J]. Telecommunications science, 2016, 32(8): 82-89. DOI： 10.11959/j.issn.1000-0801.2016197.

摘要

多标签分类已在很多领域得到了实际应用，所用标签大多具有很强的关联性，甚至存在非完备标签或部分标签遗失。然而，现有的多标签分类算法难以同时处理这两种情况。基于此，提出一种新的概率模型处理方法，实现同时对具有标签关联性和遗失标签情况进行多标签分类。该方法可以自动获知和掌握多标签的关联性。此外，通过整合遗失的标签信息，该方法能够提供一个自适应策略来处理遗失的标签。在完备标签和非完备标签的数据上进行实验，结果表明，与现有的多标签分类算法相比，提出的方法得到了较好的分类预测评价值。

Abstract

Multi-label classification methods have been applied in many real-world fields，in which the labels may have strong relevance and some of them even are incomplete or missing.However，existing multi-label classification algorithms are unable to handle both issues simultaneously.A new probabilistic model that can automatically learn and exploit multi-label relevance was proposed on label relevance and missing label classification simultaneously.By integrating out the missing information，it also provides a disciplined approach to handle missing labels.Experiments on a number of real world data sets with both complete and incomplete labels demonstrated that the proposed method can achieve higher classification and prediction evaluation scores than the existing multi-label classification algorithms.

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