A novel approach for learning label correlation with application to feature selection of multi-label data
type: Item
instance of: scholarly article
title: A novel approach for learning label correlation with application to feature selection of multi-label data
zbMATH Open document ID: 1456.68145
author: Xiaoya Che
author: Ju-Sheng Mi
author: Degang Chen
published in: Information Sciences
publication date: 2021-03-18T00:00:00Z
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 62H30
zbMATH DE Number: 7325076
zbMATH Keywords: multi-label learning
zbMATH Keywords: global label correlation
zbMATH Keywords: local label correlation
zbMATH Keywords: feature selection
describes a project that uses: MULAN
describes a project that uses: BoosTexter
describes a project that uses: ML-KNN
MaRDI profile type: Publication
full work available at URL: j.ins.2019.10.022
OpenAlex ID: W2980604334
cites work: Q5396684
cites work: A novel attribute reduction approach for multi-label data based on rough set theory
cites work: ML-KNN: A lazy learning approach to multi-label learning
cites work: BoosTexter: A boosting-based system for text categorization
cites work: Neighborhood rough sets based multi-label classification for automatic image annotation
cites work: Rough sets
Wikidata QID: Q127092165
DOI: 10.1016/J.INS.2019.10.022
description: scientific article; zbMATH DE number 7325076