Digitala Vetenskapliga Arkivet

Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Synergy Conformal Prediction for Regression
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap. Uppsala universitet, Science for Life Laboratory, SciLifeLab. (Farmbio)
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap. Uppsala universitet, Science for Life Laboratory, SciLifeLab. (Farmbio)ORCID-id: 0000-0002-8083-2864
(engelsk)Manuskript (preprint) (Annet vitenskapelig)
Abstract [en]

Large and distributed data sets pose many challenges for machine learning, including requirements on computational resources and training time. One approach is to train multiple models in parallel on subsets of data and aggregate the resulting predictions. Large data sets can then be partitioned into smaller chunks, and for distributed data the need for pooling can be avoided. Combining results from conformal predictors using synergy rules has been shown to have advantageous properties for classification problems. In this paper we extend the methodology to regression problems, and we show that it produces valid and efficient predictors compared to inductive conformal predictors and cross-conformal predictors for 10 different data sets from the UCI machine learning repository using three different machine learning methods. The approach offers a straightforward and compelling alternative to pooling data, such as when working in distributed environments.

Emneord [en]
Conformal Prediction, Machine Learning, Regression, Synergy, Ensemble Methods
HSV kategori
Forskningsprogram
Administrativ databehandling
Identifikatorer
URN: urn:nbn:se:uu:diva-377134OAI: oai:DiVA.org:uu-377134DiVA, id: diva2:1288708
Tilgjengelig fra: 2019-02-14 Laget: 2019-02-14 Sist oppdatert: 2019-02-14bibliografisk kontrollert

Open Access i DiVA

fulltext(312 kB)92 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 312 kBChecksum SHA-512
fb49cbc0369204bf25e05c761bfe81eb57c4a411d58c7ca7497be18a6ef8b727fedc9f8999994e5535fc7ad32ccfd90cde0b5ad4e34aa2518cc5aea4c75a8d18
Type fulltextMimetype application/pdf

Søk i DiVA

Av forfatter/redaktør
Gauraha, NiharikaSpjuth, Ola
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 92 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

urn-nbn

Altmetric

urn-nbn
Totalt: 254 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf