Package: ForestDisc
Type: Package
Title: Forest Discretization
Version: 0.1.0
Author: Haddouchi Maïssae
Maintainer: Haddouchi Maïssae <maissaem7@gmail.com>
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>.
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
Imports: randomForest, nloptr, moments, stats
NeedsCompilation: no
Packaged: 2020-03-10 19:53:40 UTC; Maissae
Repository: CRAN
Date/Publication: 2020-03-19 13:00:21 UTC
Built: R 4.6.0; ; 2026-04-25 19:00:04 UTC; unix
