Entropy Reweighted Conformal Classification

Conformal Prediction (CP) is a powerful framework for constructing prediction sets with guaranteed coverage. However, recent studies have shown that integrating confidence calibration with CP can lead to a degradation in efficiency. In this paper, We propose an adaptive approach that considers the c...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2024-07
Main Authors: Luo, Rui, Colombo, Nicolo
Format: Article
Language:English
Subjects:
Classification
Efficiency
Entropy
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator Luo, Rui
Colombo, Nicolo
description Conformal Prediction (CP) is a powerful framework for constructing prediction sets with guaranteed coverage. However, recent studies have shown that integrating confidence calibration with CP can lead to a degradation in efficiency. In this paper, We propose an adaptive approach that considers the classifier's uncertainty and employs entropy-based reweighting to enhance the efficiency of prediction sets for conformal classification. Our experimental results demonstrate that this method significantly improves efficiency.
format article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_3084542848</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3084542848</sourcerecordid><originalsourceid>FETCH-proquest_journals_30845428483</originalsourceid><addsrcrecordid>eNpjYuA0MjY21LUwMTLiYOAtLs4yMDAwMjM3MjU15mTQds0rKcovqFQISi1PzUzPKElNUXDOz0vLL8pNzFFwzkksLs5My0xOLMnMz-NhYE1LzClO5YXS3AzKbq4hzh66BUX5haWpxSXxWfmlRXlAqXhjAwsTUxMjCxMLY-JUAQB8LjGy</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3084542848</pqid></control><display><type>article</type><title>Entropy Reweighted Conformal Classification</title><source>Publicly Available Content (ProQuest)</source><creator>Luo, Rui ; Colombo, Nicolo</creator><creatorcontrib>Luo, Rui ; Colombo, Nicolo</creatorcontrib><description>Conformal Prediction (CP) is a powerful framework for constructing prediction sets with guaranteed coverage. However, recent studies have shown that integrating confidence calibration with CP can lead to a degradation in efficiency. In this paper, We propose an adaptive approach that considers the classifier's uncertainty and employs entropy-based reweighting to enhance the efficiency of prediction sets for conformal classification. Our experimental results demonstrate that this method significantly improves efficiency.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Classification ; Efficiency ; Entropy</subject><ispartof>arXiv.org, 2024-07</ispartof><rights>2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/3084542848?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>777,781,25734,36993,44571</link.rule.ids></links><search><creatorcontrib>Luo, Rui</creatorcontrib><creatorcontrib>Colombo, Nicolo</creatorcontrib><title>Entropy Reweighted Conformal Classification</title><title>arXiv.org</title><description>Conformal Prediction (CP) is a powerful framework for constructing prediction sets with guaranteed coverage. However, recent studies have shown that integrating confidence calibration with CP can lead to a degradation in efficiency. In this paper, We propose an adaptive approach that considers the classifier's uncertainty and employs entropy-based reweighting to enhance the efficiency of prediction sets for conformal classification. Our experimental results demonstrate that this method significantly improves efficiency.</description><subject>Classification</subject><subject>Efficiency</subject><subject>Entropy</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNpjYuA0MjY21LUwMTLiYOAtLs4yMDAwMjM3MjU15mTQds0rKcovqFQISi1PzUzPKElNUXDOz0vLL8pNzFFwzkksLs5My0xOLMnMz-NhYE1LzClO5YXS3AzKbq4hzh66BUX5haWpxSXxWfmlRXlAqXhjAwsTUxMjCxMLY-JUAQB8LjGy</recordid><startdate>20240724</startdate><enddate>20240724</enddate><creator>Luo, Rui</creator><creator>Colombo, Nicolo</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20240724</creationdate><title>Entropy Reweighted Conformal Classification</title><author>Luo, Rui ; Colombo, Nicolo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_30845428483</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Classification</topic><topic>Efficiency</topic><topic>Entropy</topic><toplevel>online_resources</toplevel><creatorcontrib>Luo, Rui</creatorcontrib><creatorcontrib>Colombo, Nicolo</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Engineering Database</collection><collection>Publicly Available Content (ProQuest)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Luo, Rui</au><au>Colombo, Nicolo</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Entropy Reweighted Conformal Classification</atitle><jtitle>arXiv.org</jtitle><date>2024-07-24</date><risdate>2024</risdate><eissn>2331-8422</eissn><abstract>Conformal Prediction (CP) is a powerful framework for constructing prediction sets with guaranteed coverage. However, recent studies have shown that integrating confidence calibration with CP can lead to a degradation in efficiency. In this paper, We propose an adaptive approach that considers the classifier's uncertainty and employs entropy-based reweighting to enhance the efficiency of prediction sets for conformal classification. Our experimental results demonstrate that this method significantly improves efficiency.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier EISSN: 2331-8422
ispartof arXiv.org, 2024-07
issn 2331-8422
language eng
recordid cdi_proquest_journals_3084542848
source Publicly Available Content (ProQuest)
subjects Classification
Efficiency
Entropy
title Entropy Reweighted Conformal Classification
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-17T15%3A13%3A25IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=Entropy%20Reweighted%20Conformal%20Classification&rft.jtitle=arXiv.org&rft.au=Luo,%20Rui&rft.date=2024-07-24&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E3084542848%3C/proquest%3E%3Cgrp_id%3Ecdi_FETCH-proquest_journals_30845428483%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=3084542848&rft_id=info:pmid/&rfr_iscdi=true