SwePub
Sök i LIBRIS databas

  Extended search

onr:"swepub:oai:DiVA.org:su-204384"
 

Search: onr:"swepub:oai:DiVA.org:su-204384" > A divisive hierarch...

  • 1 of 1
  • Previous record
  • Next record
  •    To hitlist
  • Barmpas, Petros (author)

A divisive hierarchical clustering methodology for enhancing the ensemble prediction power in large scale population studies : the ATHLOS project

  • Article/chapterEnglish2022

Publisher, publication year, extent ...

  • 2022-04-18
  • Springer Science and Business Media LLC,2022
  • printrdacarrier

Numbers

  • LIBRIS-ID:oai:DiVA.org:su-204384
  • https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-204384URI
  • https://doi.org/10.1007/s13755-022-00171-1DOI
  • http://kipublications.ki.se/Default.aspx?queryparsed=id:149295128URI

Supplementary language notes

  • Language:English
  • Summary in:English

Part of subdatabase

Classification

  • Subject category:ref swepub-contenttype
  • Subject category:art swepub-publicationtype

Notes

  • The ATHLOS cohort is composed of several harmonized datasets of international groups related to health and aging. As a result, the Healthy Aging index has been constructed based on a selection of variables from 16 individual studies. In this paper, we consider additional variables found in ATHLOS and investigate their utilization for predicting the Healthy Aging index. For this purpose, motivated by the volume and diversity of the dataset, we focus our attention upon data clustering, where unsupervised learning is utilized to enhance prediction power. Thus we show the predictive utility of exploiting hidden data structures. In addition, we demonstrate that imposed computation bottlenecks can be surpassed when using appropriate hierarchical clustering, within a clustering for ensemble classification scheme, while retaining prediction benefits. We propose a complete methodology that is evaluated against baseline methods and the original concept. The results are very encouraging suggesting further developments in this direction along with applications in tasks with similar characteristics. A straightforward open source implementation for the R project is also provided (https://github.com/Petros-Barmpas/HCEP).

Subject headings and genre

Added entries (persons, corporate bodies, meetings, titles ...)

  • Tasoulis, Sotiris (author)
  • Vrahatis, Aristidis G. (author)
  • Georgakopoulos, Spiros V. (author)
  • Anagnostou, Panagiotis (author)
  • Prina, Matthew (author)
  • Ayuso-Mateos, José Luis (author)
  • Bickenbach, Jerome (author)
  • Bayes, Ivet (author)
  • Bobak, Martin (author)
  • Caballero, Francisco Félix (author)
  • Chatterji, Somnath (author)
  • Egea-Cortés, Laia (author)
  • García-Esquinas, Esther (author)
  • Leonardi, Matilde (author)
  • Koskinen, Seppo (author)
  • Koupil, IlonaStockholms universitet,Centrum för forskning om ojämlikhet i hälsa (CHESS),Karolinska Institutet, Sweden(Swepub:su)ikoup (author)
  • Paja̧k, Andrzej (author)
  • Prince, Martin (author)
  • Sanderson, Warren (author)
  • Scherbov, Sergei (author)
  • Tamosiunas, Abdonas (author)
  • Galas, Aleksander (author)
  • Haro, Josep Maria (author)
  • Sanchez-Niubo, Albert (author)
  • Plagianakos, Vassilis P. (author)
  • Panagiotakos, Demosthenes (author)
  • Stockholms universitetCentrum för forskning om ojämlikhet i hälsa (CHESS) (creator_code:org_t)

Related titles

  • In:Health Information Science and Systems: Springer Science and Business Media LLC10:12047-2501

Internet link

Find in a library

To the university's database

  • 1 of 1
  • Previous record
  • Next record
  •    To hitlist

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Close

Copy and save the link in order to return to this view