Abstract
We present a fast potential decomposition algorithm that seeks for proportionality in a probability tree. We give a measure that determines the accuracy of a decomposition in case that exact factorization is not possible. This measure can be used to decide the variable with respect to which a tree should be factorized in order to obtain the most accurate decomposed model.
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Cano, A., Gómez-Olmedo, M., Pérez-Ariza, C.B., Salmerón, A. (2010). Fast Factorization of Probability Trees and Its Application to Recursive Trees Learning. In: Borgelt, C., et al. Combining Soft Computing and Statistical Methods in Data Analysis. Advances in Intelligent and Soft Computing, vol 77. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14746-3_9
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DOI: https://doi.org/10.1007/978-3-642-14746-3_9
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