An ensemble method using credal decision trees

Joaquín Abellán and Andrés R. Masegosa

European Journal of Operational Research, 2010, vol. 205, issue 1, 218-226

Abstract: Supervised classification learning can be considered as an important tool for decision support. In this paper, we present a method for supervised classification learning, which ensembles decision trees obtained via convex sets of probability distributions (also called credal sets) and uncertainty measures. Our method forces the use of different decision trees and it has mainly the following characteristics: it obtains a good percentage of correct classifications and an improvement in time of processing compared with known classification methods; it not needs to fix the number of decision trees to be used; and it can be parallelized to apply it on very large data sets.

Keywords: Imprecise; probabilities; Credal; sets; Imprecise; Dirichlet; model; Uncertainty; measures; Supervised; classification; Decision; trees (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:205:y:2010:i:1:p:218-226

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