About: A survey of genetic algorithms for multi-label classification

Not logged in : Login

(Sponging disallowed)

Facets (new session)
Description
Metadata
Settings
- Rule:
- Inverse Functional Properties:
- "Same As":

About: A survey of genetic algorithms for multi-label classification Goto Sponge NotDistinct Permalink

An Entity of Type : bibo:AcademicArticle, within Data Space : demo.openlinksw.com associated with source document(s)

Attributes	Values
type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #74161 A survey of genetic algorithms for multi-label classification
sameAs	A survey of genetic algorithms for multi-label classification
http://eprints.org/ontology/hasAccepted	A survey of genetic algorithms for multi-label classification (PDF)
http://eprints.org/ontology/hasDocument	A survey of genetic algorithms for multi-label classification (PDF) A survey of genetic algorithms for multi-label classification (Other) A survey of genetic algorithms for multi-label classification (Other) A survey of genetic algorithms for multi-label classification (Other) A survey of genetic algorithms for multi-label classification (Other) A survey of genetic algorithms for multi-label classification (Other)
dc:hasVersion	A survey of genetic algorithms for multi-label classification (PDF)
Title	A survey of genetic algorithms for multi-label classification
described by	https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2018-10-04
Creator	Alexandre Plastino A. Freitas E.C. Goncalves
status	peer reviewed published
Publisher	IEEE
abstract	In recent years, multi-label classification (MLC) has become an emerging research topic in big data analytics and machine learning. In this problem, each object of a dataset may belong to multiple class labels and the goal is to learn a classification model that can infer the correct labels of new, previously unseen, objects. This paper presents a survey of genetic algorithms (GAs) designed for MLC tasks. The study is organized in three parts. First, we propose a new taxonomy focused on GAs for MLC. In the second part, we provide an up-to-date overview of the work in this area, categorizing the approaches identified in the literature with respect to the taxonomy. In the third and last part, we discuss some new ideas for combining GAs with MLC.
Is Part Of	Proceedings of the 2018 IEEE Congress on Evolutionary Computation (CEC 2018) https://kar.kent.ac.uk/id/repository
Subject	Q335 Artificial intelligence
list of authors	https://kar.kent.ac.uk/id/eprint/74161#authors
presented at	2018 IEEE Congress on Evolutionary Computation (CEC 2018)
is topic of	https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #74161 A survey of genetic algorithms for multi-label classification

Faceted Search & Find service v1.17_git144 as of Jul 26 2024

Alternative Linked Data Documents: iSPARQL | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 08.03.3331 as of Aug 25 2024, on Linux (x86_64-ubuntu_noble-linux-glibc2.38-64), Single-Server Edition (378 GB total memory, 53 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software