Reference-point-based multi-objective optimization algorithm with opposition-based voting scheme for multi-label feature selection
type: Item
instance of: scholarly article
title: Reference-point-based multi-objective optimization algorithm with opposition-based voting scheme for multi-label feature selection
zbMATH Open document ID: 1475.68261
author: Azam Asilian Bidgoli
author: Hossein Ebrahimpour-Komleh
author: Shahryar Rahnamayan
published in: Information Sciences
publication date: 2021-12-02T00:00:00Z
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 68W50
Mathematics Subject Classification ID: 90C29
Mathematics Subject Classification ID: 90C59
zbMATH DE Number: 7437552
zbMATH Keywords: multi-label classification
zbMATH Keywords: feature selection
zbMATH Keywords: multi-objective optimization
zbMATH Keywords: evolutionary algorithm
zbMATH Keywords: opposition-based computation
describes a project that uses: MOEA/D
describes a project that uses: MULAN
describes a project that uses: ML-KNN
describes a project that uses: SPEA2
MaRDI profile type: Publication
full work available at URL: j.ins.2020.08.004
OpenAlex ID: W3047822990
cites work: 10.1162/153244303322753616
cites work: Running time analysis of evolutionary algorithms on a simplified multiobjective knapsack problem
cites work: Q4802571
cites work: ML-KNN: A lazy learning approach to multi-label learning
DOI: 10.1016/J.INS.2020.08.004
description: scientific article; zbMATH DE number 7437552