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Effective Utilization of Data in Inductive Conformal Prediction using Ensembles of Neural Networks
Högskolan i Borås, Institutionen Handels- och IT-högskolan.ORCID iD: 0000-0003-0274-9026
Högskolan i Borås, Institutionen Handels- och IT-högskolan.ORCID iD: 0000-0003-0412-6199
Högskolan i Borås, Institutionen Handels- och IT-högskolan.
2013 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Conformal prediction is a new framework producing region predictions with a guaranteed error rate. Inductive conformal prediction (ICP) was designed to significantly reduce the computational cost associated with the original transductive online approach. The drawback of inductive conformal prediction is that it is not possible to use all data for training, since it sets aside some data as a separate calibration set. Recently, cross-conformal prediction (CCP) and bootstrap conformal prediction (BCP) were proposed to overcome that drawback of inductive conformal prediction. Unfortunately, CCP and BCP both need to build several models for the calibration, making them less attractive. In this study, focusing on bagged neural network ensembles as conformal predictors, ICP, CCP and BCP are compared to the very straightforward and cost-effective method of using the out-of-bag estimates for the necessary calibration. Experiments on 34 publicly available data sets conclusively show that the use of out-of-bag estimates produced the most efficient conformal predictors, making it the obvious preferred choice for ensembles in the conformal prediction framework.

Place, publisher, year, edition, pages
IEEE, 2013.
Keywords [en]
Data mining, Machine Learning
National Category
Computer Sciences Computer and Information Sciences
Identifiers
URN: urn:nbn:se:hj:diva-38091OAI: oai:DiVA.org:hj-38091DiVA, id: diva2:1163330
Conference
International Joint Conference on Neural Networks, Dallas, TX, USA, August 4-9, 2013.
Note

Sponsorship:

Swedish Foundation for Strategic

Research through the project High-Performance Data Mining for Drug Effect

Detection (IIS11-0053) and the Knowledge Foundation through the project

Big Data Analytics by Online Ensemble Learning (20120192)

Available from: 2017-12-06 Created: 2017-12-06 Last updated: 2018-01-13Bibliographically approved

Open Access in DiVA

fulltext(171 kB)22 downloads
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Authority records BETA

Löfström, TuveJohansson, Ulf

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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Language
  • de-DE
  • en-GB
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  • Other locale
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Output format
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