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Conformal Prediction Using Decision Trees
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
2013 (engelsk)Inngår i: IEEE 13th International Conference on Data Mining (ICDM): Proceedings, IEEE Computer Society, 2013, 330-339 s.Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Conformal prediction is a relatively new framework in which the predictive models output sets of predictions with a bound on the error rate, i.e., in a classification context, the probability of excluding the correct class label is lower than a predefined significance level. An investigation of the use of decision trees within the conformal prediction framework is presented, with the overall purpose to determine the effect of different algorithmic choices, including split criterion, pruning scheme and way to calculate the probability estimates. Since the error rate is bounded by the framework, the most important property of conformal predictors is efficiency, which concerns minimizing the number of elements in the output prediction sets. Results from one of the largest empirical investigations to date within the conformal prediction framework are presented, showing that in order to optimize efficiency, the decision trees should be induced using no pruning and with smoothed probability estimates. The choice of split criterion to use for the actual induction of the trees did not turn out to have any major impact on the efficiency. Finally, the experimentation also showed that when using decision trees, standard inductive conformal prediction was as efficient as the recently suggested method cross-conformal prediction. This is an encouraging results since cross-conformal prediction uses several decision trees, thus sacrificing the interpretability of a single decision tree.

sted, utgiver, år, opplag, sider
IEEE Computer Society, 2013. 330-339 s.
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
URN: urn:nbn:se:su:diva-97717DOI: 10.1109/ICDM.2013.85ISBN: 978-0-7685-5108-1 (tryckt)OAI: oai:DiVA.org:su-97717DiVA: diva2:679947
Konferanse
IEEE International Conference on Data Mining, Dallas, Texas, December 7-10, 2013
Tilgjengelig fra: 2013-12-17 Laget: 2013-12-17 Sist oppdatert: 2014-02-25bibliografisk kontrollert

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