[1]沈小霞,许哲源,於东军,等.标记分布集成学习[J].南京理工大学学报(自然科学版),2020,44(06):660-668.[doi:10.14177/j.cnki.32-1397n.2020.44.06.004]
 Shen Xiaoxia,Xu Zheyuan,Yu Dongjun,et al.Label distribution ensemble learning[J].Journal of Nanjing University of Science and Technology,2020,44(06):660-668.[doi:10.14177/j.cnki.32-1397n.2020.44.06.004]
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标记分布集成学习()
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
44卷
期数:
2020年06期
页码:
660-668
栏目:
出版日期:
2020-12-31

文章信息/Info

Title:
Label distribution ensemble learning
文章编号:
1005-9830(2020)06-0660-09
作者:
沈小霞许哲源於东军贾修一
南京理工大学 计算机科学与工程学院,江苏 南京 210094
Author(s):
Shen XiaoxiaXu ZheyuanYu DongjunJia Xiuyi
School of Computer Science and Engineering,Nanjing University of Scienceand Technology,Nanjing 210094,China
关键词:
标记分布学习 自适应提升 排序损失 集成学习
Keywords:
label distribution learning adaboost sort loss ensemble learning
分类号:
TP391
DOI:
10.14177/j.cnki.32-1397n.2020.44.06.004
摘要:
标记分布学习是一种新型的学习范式,该文提出了一种适用于标记分布问题形式的Adaboost集成算法,能够有效地提升各种单独算法的预测精度。该文设计了一种新的用于反映排序损失的评价指标SortLoss。该文将Adaboost应用在标记分布学习问题上。实验结果表明,该文设计的Adaboost-LDL集成框架在13个真实数据集上能够显著提升标记分布学习算法的预测精度,该文的方法将排序损失指标SortLoss平均降低至原先的41.2%,KL散度指标平均降低至原先的68.5%。
Abstract:
Label distribution learning is a new learning paradigm. This paper proposes an Adaboost ensemble algorithm suitable for the form of label distribution learning problem,which can effectively improve the prediction accuracy of various individual algorithms. This paper designs a new evaluation index SortLoss to reflect the sorting loss. This paper applys Adaboost to label distribution learning problems. The experimental results show that the Adaboost-LDL integration algorithm proposed here can significantly improve various existing LDL algorithms on 13 real data sets. Compared with the pre-integration algorithm,the sorting loss can be reduced to an average of 41.2% and the Kullback-Leibler Divergence indicator is reduced to an average of 68.5%.

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备注/Memo

备注/Memo:
收稿日期:2020-09-14 修回日期:2020-10-12
基金项目:国家自然科学基金(61773208); 江苏省自然科学基金(BK20191287); 中央高校基本科研业务费专项资金(30920021131)
作者简介:沈小霞(1996-),女,硕士生,主要研究方向:机器学习和数据挖掘,E-mail:xiaoxiashen@njust.edu.cn; 通讯作者:贾修一(1983-),男,博士,副教授,主要研究方向:粒计算与不确定性分析、机器学习和数据挖掘,E-mail:jiaxy@njust.edu.cn。
引文格式:沈小霞,许哲源,於东军,等. 标记分布集成学习[J]. 南京理工大学学报,2020,44(6):660-668.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2020-12-30