[1]袁京洲,高 昊,周家特,等.基于树结构的层次性多示例多标记学习[J].南京师范大学学报(自然科学版),2019,42(03):80-87.[doi:10.3969/j.issn.1001-4616.2019.03.011]
　Yuan Jingzhou,Gao Hao,Zhou Jiate,et al.A Hierarchical Multi-Instance Multi-Label Learning forTree Structure Among Labels[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(03):80-87.[doi:10.3969/j.issn.1001-4616.2019.03.011]
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基于树结构的层次性多示例多标记学习()
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《南京师范大学学报》（自然科学版）[ISSN:1001-4616/CN:32-1239/N]

卷:
第42卷
期数:
2019年03期
页码:
80-87
栏目:
·全国机器学习会议论文专栏·
出版日期:
2019-09-30

文章信息/Info

Title:
A Hierarchical Multi-Instance Multi-Label Learning forTree Structure Among Labels
文章编号:
1001-4616(2019)03-0080-08
作者:
袁京洲1; 高 昊1; 周家特1; 冯巧遇2; 吴建盛1
(1.南京邮电大学地理与生物信息学院,江苏 南京 210023)(2.南京邮电大学通信与信息工程学院,江苏 南京 210023)
Author(s):
Yuan Jingzhou1; Gao Hao1; Zhou Jiate1; Feng Qiaoyu2; Wu Jiansheng1
(1.School of Geographic and Biologic Information,Nanjing University of Posts and Telecommunications,Nanjing 210023,China)(2.College of Telecommunications and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210023,China)
关键词:
层次性多示例多标记学习; 树结构; G蛋白偶联受体; 生物学功能; 多示例学习
Keywords:
hierarchical multi-instance multi-label learning; tree structure; G protein-coupled receptors; biological functions; multi-instance leanring
分类号:
TP399
DOI:
10.3969/j.issn.1001-4616.2019.03.011
文献标志码:
A
摘要:
针对多示例多标记学习中标记间树结构的问题,将多示例学习、多标记学习和树结构标记优化方法有机融合,提出了基于树结构标记的层次性多示例多标记学习方法TreeMIML. TreeMIML先将样本中的多个示例转化为单示例,然后通过多标记学习得到新样本的标记,最后通过树结构标记优化方法学习样本的最终标记. 实验结果证明,TreeMIML方法在G蛋白偶联受体的生物学功能预测上获得了很好的分类性能,优于目前最好的多示例多标记学习和多标记学习方法.
Abstract:
This paper proposed a novel hierarchical multi-instance multi-label learning algorithm named TreeMIML to solve the challenge of tree structure among labels in multi-instance multi-label learning(MIML),by integrating multi-instance learning,multi-label learning and tree-structure optimization scheme. TreeMIML first converts multiple instances in each sample into single instance,then obtains sample outputs by multi-label learning,and finally optimizes the outputs to obtain the labels of unseen samples by a tree-structure optimization method. The experimental results show that our TreeMIML algorithm achieves good classification performance in predicting biological functions of G protein-coupled receptors,which is superior to state-of-the-art multi-instance multi-label learning and multi-label learning methods.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2019-07-05.基金项目:国家自然科学基金(61872198、81771478、61571233)、江苏省高校自然科学基金(18KJB416005)、江苏省高等学校自然科学研究项目(17KJA510003)、南京邮电大学科研基金(NY218092). 通讯联系人:吴建盛,副教授,研究方向:机器学习和生物信息学. E-mail:jansen@njupt.edu.cn
更新日期/Last Update: 2019-09-30