Generating High-Quality Prediction Intervals for Regression Tasks via Fuzzy C-Means Clustering-Based Conformal Prediction

Saleh Msaddi, Tufan Kumbasar*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Accurately assessing uncertainty and prediction of a regression model is essential for making informed decisions, especially in high-risk tasks. Conformal Prediction (CP) is a powerful distribution-free uncertainty quantification framework for building such models as it is capable to transform a single-point prediction of any machine learning model into a Prediction Interval (PI) with a guarantee of encompassing the true value for specified levels of confidence. On the other hand, to generate high-quality PIs, the PIs should be as narrow as possible while enveloping a certain amount of uncertainty (i.e. confidence level). The generated width of the PIs mainly depends on the nonconformity measure used within the CP. In this study, we propose two novel Fuzzy c-Means Clustering (FCM) based nonconformity measures for CP with nearest neighbors to learn distribution-free and high-quality PIs for regression. The proposed approach generates tight PIs by evaluating the degree of nonconformity of a new data point compared to the so-called calibration points via Fuzzy Sets (FSs). From the calibration dataset, we extract representative FSs via FCM and assign every test point alongside the nearest neighbors within the calibration dataset with membership grades to adapt the nonconformity measure. To evaluate the performance, we present statistical comparisons and demonstrate that the proposed FCM-based nonconformity measures result in high-quality PIs.

Original languageEnglish
Title of host publicationIntelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference
EditorsCengiz Kahraman, Irem Ucal Sari, Basar Oztaysi, Sezi Cevik Onar, Selcuk Cebi, A. Çağrı Tolga
PublisherSpringer Science and Business Media Deutschland GmbH
Pages532-539
Number of pages8
ISBN (Print)9783031397769
DOIs
Publication statusPublished - 2023
EventIntelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference - Istanbul, Turkey
Duration: 22 Aug 202324 Aug 2023

Publication series

NameLecture Notes in Networks and Systems
Volume759 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

ConferenceIntelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference
Country/TerritoryTurkey
CityIstanbul
Period22/08/2324/08/23

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Funding

This work was supported by the BAGEP Award of the Science Academy.

FundersFunder number
Bilim Akademisi

    Keywords

    • Conformal Prediction
    • Fuzzy Clustering
    • Uncertainty Quantification

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