A Unified Multi-label Relationship Learning

R. Rastogi, Simran Popli, Nima Dorji Moktan, Sweta Sharma
{"title":"A Unified Multi-label Relationship Learning","authors":"R. Rastogi, Simran Popli, Nima Dorji Moktan, Sweta Sharma","doi":"10.1109/ICCSE.2019.8845526","DOIUrl":null,"url":null,"abstract":"Multi-label learning belongs to the class of supervised learning wherein each sample is represented by a single instance and is associated with a set of relevant labels. Many realworld applications like medical diagnosis and image classification involve multi-label classification wherein label correlations are essential to the performance of the classifier. To utilize this correlation among labels, in this paper, we propose a novel model termed as Unified Multi-label Relationship Learning (UMRL) which considers the explicit and implicit correlation inherent in data to build an effective learning model. We adopt the Accelerated Gradient Method (AGM) to train the underlying optimization model efficiently. Extensive experimental comparisons to state-of-the-art multi-label algorithms demonstrate the validity and effectiveness of our proposed approach.","PeriodicalId":351346,"journal":{"name":"2019 14th International Conference on Computer Science & Education (ICCSE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 14th International Conference on Computer Science & Education (ICCSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSE.2019.8845526","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Multi-label learning belongs to the class of supervised learning wherein each sample is represented by a single instance and is associated with a set of relevant labels. Many realworld applications like medical diagnosis and image classification involve multi-label classification wherein label correlations are essential to the performance of the classifier. To utilize this correlation among labels, in this paper, we propose a novel model termed as Unified Multi-label Relationship Learning (UMRL) which considers the explicit and implicit correlation inherent in data to build an effective learning model. We adopt the Accelerated Gradient Method (AGM) to train the underlying optimization model efficiently. Extensive experimental comparisons to state-of-the-art multi-label algorithms demonstrate the validity and effectiveness of our proposed approach.
统一的多标签关系学习
多标签学习属于监督学习,其中每个样本由单个实例表示,并与一组相关标签相关联。许多现实世界的应用,如医学诊断和图像分类,都涉及多标签分类,其中标签相关性对分类器的性能至关重要。为了利用标签之间的这种相关性,本文提出了一种称为统一多标签关系学习(UMRL)的新模型,该模型考虑了数据中固有的显式和隐式相关性来构建有效的学习模型。我们采用加速梯度法(AGM)有效地训练底层优化模型。广泛的实验比较最先进的多标签算法证明了我们提出的方法的有效性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信