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Effective Utilization of Data in Inductive Conformal Prediction
University of Borås, School of Business and IT. (CSL@BS)
University of Borås, School of Business and IT. (CSL@BS)
University of Borås, School of Business and IT. (CSL@BS)
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.
Keyword [en]
Data mining, Machine Learning
National Category
Computer Science Computer and Information Science
Identifiers
URN: urn:nbn:se:hb:diva-7058Local ID: 2320/12923OAI: oai:DiVA.org:hb-7058DiVA: diva2:887765
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: 2015-12-22 Created: 2015-12-22 Last updated: 2017-05-02

Open Access in DiVA

fulltext(171 kB)108 downloads
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Löfström, TuveJohansson, UlfBoström, Henrik
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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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