loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Niharika Gauraha and Ola Spjuth

Affiliation: Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden

Keyword(s): Conformal Prediction, Machine Learning, Regression, Synergy, Ensemble Methods.

Abstract: 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 sets 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.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.141.202.187

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Gauraha, N. and Spjuth, O. (2021). Synergy Conformal Prediction for Regression. In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-486-2; ISSN 2184-4313, SciTePress, pages 212-221. DOI: 10.5220/0010229402120221

@conference{icpram21,
author={Niharika Gauraha. and Ola Spjuth.},
title={Synergy Conformal Prediction for Regression},
booktitle={Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2021},
pages={212-221},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010229402120221},
isbn={978-989-758-486-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Synergy Conformal Prediction for Regression
SN - 978-989-758-486-2
IS - 2184-4313
AU - Gauraha, N.
AU - Spjuth, O.
PY - 2021
SP - 212
EP - 221
DO - 10.5220/0010229402120221
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic