About: Nested Trees for Longitudinal Classification

Not logged in : Login

(Sponging disallowed)

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

About: Nested Trees for Longitudinal Classification Goto Sponge NotDistinct Permalink

An Entity of Type : bibo:AcademicArticle, within Data Space : linkeddata.uriburner.com:28898 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 #92832 Nested Trees for Longitudinal Classification
http://eprints.org/ontology/hasAccepted	Nested Trees for Longitudinal Classification (PDF)
http://eprints.org/ontology/hasDocument	Nested Trees for Longitudinal Classification (PDF) Nested Trees for Longitudinal Classification (Other) Nested Trees for Longitudinal Classification (Other) Nested Trees for Longitudinal Classification (Other) Nested Trees for Longitudinal Classification (Other) Nested Trees for Longitudinal Classification (Other)
dc:hasVersion	Nested Trees for Longitudinal Classification (PDF)
Title	Nested Trees for Longitudinal Classification
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
Date	2021-12-12
Creator	A. Freitas Fernando E.B. Otero Sergey Ovchinnik
status	forthcoming peer reviewed
abstract	Longitudinal datasets contain repeated measurements of the same variables at different points in time. Longitudinal data mining algorithms aim to utilize such datasets to extract interesting knowledge and produce useful models. Many existing longitudinal classification methods either dismiss the longitudinal aspect of the data during model construction or produce complex models that are scarcely interpretable. We propose a new longitudinal classification algorithm based on decision trees, named Nested Trees. It utilizes a unique longitudinal model construction method that is fully aware of the longitudinal aspect of the predictive attributes (variables) and constructs tree nodes that make decisions based on a longitudinal attribute as a whole, considering measurements of that attribute across multiple time points. The algorithm was evaluated using 10 classification tasks based on the English Longitudinal Study of Ageing (ELSA) data.
Is Part Of	https://kar.kent.ac.uk/id/repository
Subject	Q335 Artificial intelligence
list of authors	https://kar.kent.ac.uk/id/eprint/92832#authors
presented at	37th ACM/SIGAPP Symposium On Applied Computing
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #92832 Nested Trees for Longitudinal 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, 15 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software