.png){kind=icon}
返回
Topic-Based Instance and Feature Selection in Multilabel Classification
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2020.3027745.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multilabel learning has been extensively studied in the past years, as it has many applications in different domains. It aims at annotating the labels for unseen data according to training data, which are often high dimensional in both instance and feature levels. The training data often have noisy and redundant information on these two levels. As an effective data preprocessing step, instance and feature selection should both be performed to find relevant training instances for each testing instance and relevant features for each label, respectively. However, most of the existing methods overlook the input-output correlation in each kind of selection. It will lead to the performance degradation. This article presents a formulation for multilabel learning from a topic view that exploits the dependence between features and labels in a topic space. We can perform effective instance and feature selection in the latent topic space, as the relationship between the input and output spaces is well captured in this space. The results from intensive experiments on various benchmarks demonstrate the effectiveness of the proposed framework.
Keyword:
Feature extraction
Correlation
Training
Testing
Learning systems
Urban areas
Training data
Input-output correlation
instance and feature selection
multilabel learning
topic
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
机构
引用论文
Learning Label-Specific Features and Class-Dependent Labels for Multi-Label Classification用于多标签分类的学习标签特定特征和类别相关标签
PREAI
分享收藏
Morphology and properties of melt-spun polycarbonate fibers containing single- and multi-wall carbon nanotubes.png)
Polymer
PolymerIF0
PREAI
分享收藏
查看更多内容