|Table of Contents|

A Hierarchical Multi-Instance Multi-Label Learning forTree Structure Among Labels(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2019年03期
Page:
80-87
Research Field:
·全国机器学习会议论文专栏·
Publishing date:

Info

Title:
A Hierarchical Multi-Instance Multi-Label Learning forTree Structure Among Labels
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)
Keywords:
hierarchical multi-instance multi-label learning; tree structure; G protein-coupled receptors; biological functions; multi-instance leanring
PACS:
TP399
DOI:
10.3969/j.issn.1001-4616.2019.03.011
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.

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Last Update: 2019-09-30