Synergy Conformal Prediction for Regression

Niharika Gauraha, Ola Spjuth

2021

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.

Paper Citation

in Harvard Style

Gauraha N. and Spjuth O. (2021). Synergy Conformal Prediction for Regression.In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-486-2, pages 212-221. DOI: 10.5220/0010229402120221

in Bibtex Style

@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 - Volume 1: ICPRAM,},
year={2021},
pages={212-221},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010229402120221},
isbn={978-989-758-486-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Synergy Conformal Prediction for Regression
SN - 978-989-758-486-2
AU - Gauraha N.
AU - Spjuth O.
PY - 2021
SP - 212
EP - 221
DO - 10.5220/0010229402120221