ForestDisc

Description

Supervised, multivariate, and non-parametric discretization algorithm based on tree ensembles learning and moment matching optimization. This version of the algorithm relies on random forest algorithm to learn a large set of split points that conserves the relationship between attributes and the target class, and on moment matching optimization to transform this set into a reduced number of cut points matching as well as possible statistical properties of the initial set of split points. For each attribute to be discretized, the set S of its related split points extracted through random forest is mapped to a reduced set C of cut points of size k. This mapping relies on minimizing, for each continuous attribute to be discretized, the distance between the four first moments of S and the four first moments of C subject to some constraints. This non-linear optimization problem is performed using k values ranging from 2 to 'max_splits', and the best solution returned correspond to the value k which optimum solution is the lowest one over the different realizations. ForestDisc is a generalization of RFDisc discretization method initially proposed by Berrado and Runger (2009) <doi:10.1109/AICCSA.2009.5069327>, and improved by Berrado et al. in 2012 by adopting the idea of moment matching optimization related by Hoyland and Wallace (2001) <doi: 10.1287/mnsc.47.2.295.9834>.

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Maintainer: ‘Haddouchi Maïssae <maissaem7@gmail.com>’

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Maintainer: ‘Haddouchi Maïssae <maissaem7@gmail.com>’

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Maintainer: ‘Haddouchi Maïssae <maissaem7@gmail.com>’

No Authors@R field in DESCRIPTION.
Please add one, modifying
  Authors@R: person(given = "Haddouchi",
                    family = "Maïssae",
                    role = c("aut", "cre"),
                    email = "maissaem7@gmail.com")
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Maintainer: ‘Haddouchi Maïssae <maissaem7@gmail.com>’

No Authors@R field in DESCRIPTION.
Please add one, modifying
  Authors@R: person(given = "Haddouchi",
                    family = "Maïssae",
                    role = c("aut", "cre"),
                    email = "maissaem7@gmail.com")
as necessary.

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R Observatory began tracking this package on Mar 10, 2026; it first appeared on CRAN Mar 19, 2020. Releases before tracking aren’t shown.