Abstract
In this article, we shall present a method for combining classification trees obtained by a simple method from the imprecise Dirichlet model (IDM) and uncertainty measures on closed and convex sets of probability distributions, otherwise known as credal sets. Our combine method has principally two characteristics: it obtains a high percentage of correct classifications using a few number of classification trees and it can be parallelized to apply on very large databases.
This work has been supported by the Spanish Ministry of Science and Technology under the projects TIN2005-02516 and TIN2004-06204-C03-02; and FPU scholarship programme (AP2004-4678).
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Abellán, J., Masegosa, A.R. (2007). Combining Decision Trees Based on Imprecise Probabilities and Uncertainty Measures. In: Mellouli, K. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2007. Lecture Notes in Computer Science(), vol 4724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75256-1_46
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