ForestDisc: Forest Discretization

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.>.

Version: 0.1.0
Imports: randomForest, nloptr, moments, stats
Published: 2020-03-19
DOI: 10.32614/CRAN.package.ForestDisc
Author: Haddouchi Maïssae
Maintainer: Haddouchi Maïssae <maissaem7 at>
License: GPL (≥ 3)
NeedsCompilation: no
CRAN checks: ForestDisc results


Reference manual: ForestDisc.pdf


Package source: ForestDisc_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): ForestDisc_0.1.0.tgz, r-oldrel (arm64): ForestDisc_0.1.0.tgz, r-release (x86_64): ForestDisc_0.1.0.tgz, r-oldrel (x86_64): ForestDisc_0.1.0.tgz


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