基于双标签集的标签匹配集成学习算法

doi:10.11772/j.issn.1001-9081.2014.09.2577

计算机应用 ›› 2014, Vol. 34 ›› Issue (9): 2577-2580.DOI: 10.11772/j.issn.1001-9081.2014.09.2577

基于双标签集的标签匹配集成学习算法

张丹普¹,²,王莉莉¹,²,付忠良¹,李昕¹,²

1. 中国科学院成都计算机应用研究所，成都 610041；
2. 中国科学院大学，北京 100049

收稿日期:2014-04-02 修回日期:2014-06-08 出版日期:2014-09-01 发布日期:2014-09-30
通讯作者: 张丹普
作者简介:
张丹普(1986-),女,河南平顶山人,博士研究生,主要研究方向:机器学习、模式识别;
王莉莉(1987-),女,河南周口人,博士研究生,主要研究方向:机器学习、模式识别;
付忠良(1967-),男,重庆合川人,研究员,博士生导师,主要研究方向:机器学习、模式识别;
李昕(1985-),男,陕西汉中人,博士研究生,主要研究方向:图形图像处理、模式识别。
基金资助:
四川省科技支撑计划项目

Ensemble learning algorithm for labels matching based on pairwise labelsets

ZHANG Danpu¹,²,WANG Lili¹,²,FU Zhongliang¹,LI Xin¹,²

1. Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu Sichuan 610041, China
2. University of Chinese Academy of Sciences, Beijing 100049, China

Received:2014-04-02 Revised:2014-06-08 Online:2014-09-01 Published:2014-09-30
Contact: ZHANG Danpu

摘要/Abstract

摘要：

当标识示例的两个标签分别来源于两个标签集时，这种多标签分类问题称之为标签匹配问题，目前还没有针对标签匹配问题的学习算法。尽管可以用传统的多标签分类学习算法来解决标签匹配问题，但显然标签匹配问题有其自身特殊性。通过对标签匹配问题进行深入的研究，在连续AdaBoost(real Adaptive Boosting)算法的基础上，基于整体优化的思想，采用算法适应的方法，提出了基于双标签集的标签匹配集成学习算法，该算法能够较好地学习到标签匹配规律从而完成标签匹配。实验结果表明，与传统的多标签学习算法用于解决标签匹配问题相比，提出的新算法不仅缩小了搜索的标签空间的范围，而且最小化学习误差可以随着分类器个数的增加而降低，进而使得标签匹配分类更加快速、准确。

Abstract:

It is called labels matching problem when two labels of an instance come from two labelsets respectively in multi-label classification, however there is no any specific algorithm for solving such problem. Although the labels matching problem could be solved by tranditional multi-label classification algorithms, but this problem has its own particularity. After analyzing the labels matching problem, a new labels matching algorithm based on pairwise labelsets was proposed using adaptive method, which considered the real Adaptive Boosting (real AdaBoost) and the global optimization idea. This algorithm could learn the rule of labels matching well and complete matching. The experimental results show that, compared with the traditional algorithms, the new algorithm can not only reduce searching scope of the labels space, but also decrease the minimum learning error as the number of weak classifiers increases, and make the classification more accurate and faster.

中图分类号:

TP391.4

张丹普王莉莉付忠良李昕. 基于双标签集的标签匹配集成学习算法[J]. 计算机应用, 2014, 34(9): 2577-2580.

ZHANG Danpu WANG Lili FU Zhongliang LI Xin. Ensemble learning algorithm for labels matching based on pairwise labelsets[J]. Journal of Computer Applications, 2014, 34(9): 2577-2580.

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基于双标签集的标签匹配集成学习算法

Ensemble learning algorithm for labels matching based on pairwise labelsets

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