About: Monotonicity Detection and Enforcement in Longitudinal Classification

Not logged in : Login

(Sponging disallowed)

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

About: Monotonicity Detection and Enforcement in 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 #77372 Monotonicity Detection and Enforcement in Longitudinal Classification
sameAs	Monotonicity Detection and Enforcement in Longitudinal Classification
http://www.loc.gov...erms/relators/EDT	Max Bramer Miltos Petridis
http://eprints.org/ontology/hasAccepted	Monotonicity Detection and Enforcement in Longitudinal Classification (PDF)
http://eprints.org/ontology/hasDocument	Monotonicity Detection and Enforcement in Longitudinal Classification (PDF) Monotonicity Detection and Enforcement in Longitudinal Classification (Other) Monotonicity Detection and Enforcement in Longitudinal Classification (Other) Monotonicity Detection and Enforcement in Longitudinal Classification (Other) Monotonicity Detection and Enforcement in Longitudinal Classification (Other) Monotonicity Detection and Enforcement in Longitudinal Classification (Other)
dc:hasVersion	Monotonicity Detection and Enforcement in Longitudinal Classification (PDF)
Title	Monotonicity Detection and Enforcement in 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 https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2019-11-19
Creator	A. Freitas Fernando E.B. Otero Sergey Ovchinnik
status	peer reviewed published
Publisher	Springer
abstract	Longitudinal datasets contain repeated measurements of the same variables at different points in time, which can be used by researchers to discover useful knowledge based on the changes of the data over time. Monotonic relations often occur in real-world data and need to be preserved in data mining models in order for the models to be acceptable by users. We propose a new methodology for detecting monotonic relations in longitudinal datasets and applying them in longitudinal classification model construction. Two different approaches were used to detect monotonic relations and include them into the classification task. The proposed approaches are evaluated using data from the English Lon- gitudinal Study of Ageing (ELSA) with 10 different age-related diseases used as class variables to be predicted. A gradient boosting algorithm (XGBoost) is used for constructing classification models in two scenarios: enforcing and not enforcing the constraints. The results show that enforcement of monotonicity constraints can consistently improve the predictive accuracy of the constructed models. The produced models are fully monotonic according to the monotonicity constraints, which can have a positive impact on model acceptance in real world applications.
Is Part Of	Financial Cryptography and Data Security https://kar.kent.ac.uk/id/repository
Subject	Q335 Artificial intelligence
list of authors	https://kar.kent.ac.uk/id/eprint/77372#authors
list of editors	https://kar.kent.ac.uk/id/eprint/77372#editors
presented at	39th SGAI International Conference on Artificial Intelligence, AI 2019
volume	11927
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #77372 Monotonicity Detection and Enforcement in 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