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Evolved Decision Trees as Conformal Predictors
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)
University of Borås, School of Business and IT. (CSL@BS)
2013 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In conformal prediction, predictive models output sets of predictions with a bound on the error rate. In classification, this translates to that the probability of excluding the correct class is lower than a predefined significance level, in the long run. Since the error rate is guaranteed, the most important criterion for conformal predictors is efficiency. Efficient conformal predictors minimize the number of elements in the output prediction sets, thus producing more informative predictions. This paper presents one of the first comprehensive studies where evolutionary algorithms are used to build conformal predictors. More specifically, decision trees evolved using genetic programming are evaluated as conformal predictors. In the experiments, the evolved trees are compared to decision trees induced using standard machine learning techniques on 33 publicly available benchmark data sets, with regard to predictive performance and efficiency. The results show that the evolved trees are generally more accurate, and the corresponding conformal predictors more efficient, than their induced counterparts. One important result is that the probability estimates of decision trees when used as conformal predictors should be smoothed, here using the Laplace correction. Finally, using the more discriminating Brier score instead of accuracy as the optimization criterion produced the most efficient conformal predictions.

Place, publisher, year, edition, pages
IEEE , 2013.
Keyword [en]
Conformal prediction, Genetic programming, Data mining, Machine Learning
National Category
Computer Science Computer and Information Science
Identifiers
URN: urn:nbn:se:hb:diva-7053DOI: 10.1109/CEC.2013.6557778ISI: 000326235301102Local ID: 2320/12919ISBN: 978-1-4799-0453-2 (print)OAI: oai:DiVA.org:hb-7053DiVA: diva2:887760
Conference
IEEE Congress on Evolutionary Computation, 20-23 June 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

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Johansson, UlfKönig, RikardLöfström, TuweBoström, Henrik
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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
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  • Other locale
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Output format
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