多标签分类的广义k-标签集集成

Hung-Yi Lo, Shou-de Lin, H. Wang
{"title":"多标签分类的广义k-标签集集成","authors":"Hung-Yi Lo, Shou-de Lin, H. Wang","doi":"10.1109/ICASSP.2012.6288315","DOIUrl":null,"url":null,"abstract":"Label powerset (LP) method is one category of multi-label learning algorithms. It reduces the multi-label classification problem to a multi-class classification problem by treating each distinct combination of labels in the training set as a different class. This paper proposes a basis expansion model for multi-label classification, where a basis function is a LP classifier trained on a random k-labelset. The expansion coefficients are learned to minimize the global error between the prediction and the multi-label ground truth. We derive an analytic solution to learn the coefficients efficiently. We have conducted experiments using several benchmark datasets and compared our method with other state-of-the-art multi-label learning methods. The results show that our method has better or competitive performance against other methods.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"43 1","pages":"2061-2064"},"PeriodicalIF":0.0000,"publicationDate":"2012-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Generalized k-labelset ensemble for multi-label classification\",\"authors\":\"Hung-Yi Lo, Shou-de Lin, H. Wang\",\"doi\":\"10.1109/ICASSP.2012.6288315\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Label powerset (LP) method is one category of multi-label learning algorithms. It reduces the multi-label classification problem to a multi-class classification problem by treating each distinct combination of labels in the training set as a different class. This paper proposes a basis expansion model for multi-label classification, where a basis function is a LP classifier trained on a random k-labelset. The expansion coefficients are learned to minimize the global error between the prediction and the multi-label ground truth. We derive an analytic solution to learn the coefficients efficiently. We have conducted experiments using several benchmark datasets and compared our method with other state-of-the-art multi-label learning methods. The results show that our method has better or competitive performance against other methods.\",\"PeriodicalId\":6443,\"journal\":{\"name\":\"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)\",\"volume\":\"43 1\",\"pages\":\"2061-2064\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-03-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICASSP.2012.6288315\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASSP.2012.6288315","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

标签功率集(LP)方法是多标签学习算法的一种。它通过将训练集中每个不同的标签组合视为不同的类别,将多标签分类问题简化为多类分类问题。本文提出了一种多标签分类的基展开模型,其中基函数是在随机k-标签集上训练的LP分类器。学习扩展系数以最小化预测与多标签真实值之间的全局误差。我们推导了一个解析解来有效地学习系数。我们使用几个基准数据集进行了实验,并将我们的方法与其他最先进的多标签学习方法进行了比较。结果表明,与其他方法相比,我们的方法具有更好或更具竞争力的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Generalized k-labelset ensemble for multi-label classification
Label powerset (LP) method is one category of multi-label learning algorithms. It reduces the multi-label classification problem to a multi-class classification problem by treating each distinct combination of labels in the training set as a different class. This paper proposes a basis expansion model for multi-label classification, where a basis function is a LP classifier trained on a random k-labelset. The expansion coefficients are learned to minimize the global error between the prediction and the multi-label ground truth. We derive an analytic solution to learn the coefficients efficiently. We have conducted experiments using several benchmark datasets and compared our method with other state-of-the-art multi-label learning methods. The results show that our method has better or competitive performance against other methods.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Scalable Multilevel Quantization for Distributed Detection Linear Model-Based Intra Prediction in VVC Test Model Practical Concentric Open Sphere Cardioid Microphone Array Design for Higher Order Sound Field Capture Embedding Physical Augmentation and Wavelet Scattering Transform to Generative Adversarial Networks for Audio Classification with Limited Training Resources Improving ASR Robustness to Perturbed Speech Using Cycle-consistent Generative Adversarial Networks
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1