Class for construction a Rotation Forest. Can do classification and regression depending on the base learner.
For more information, see
Juan J. Rodriguez, Ludmila I. Kuncheva, Carlos J. Alonso (2006). Rotation Forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28(10):1619-1630. URL http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.211.
BibTeX:
@article{Rodriguez2006,
author = {Juan J. Rodriguez and Ludmila I. Kuncheva and Carlos J. Alonso},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
number = {10},
pages = {1619-1630},
title = {Rotation Forest: A new classifier ensemble method},
volume = {28},
year = {2006},
ISSN = {0162-8828},
URL = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.211}
}
Valid options are:
-N
Whether minGroup (-G) and maxGroup (-H) refer to
the number of groups or their size.
(default: false)
-G <num>
Minimum size of a group of attributes:
if numberOfGroups is true, the minimum number
of groups.
(default: 3)
-H <num>
Maximum size of a group of attributes:
if numberOfGroups is true, the maximum number
of groups.
(default: 3)
-P <num>
Percentage of instances to be removed.
(default: 50)
-F <filter specification>
Full class name of filter to use, followed
by filter options.
eg: "weka.filters.unsupervised.attribute.PrincipalComponents-R 1.0"
-S <num>
Random number seed.
(default 1)
-I <num>
Number of iterations.
(default 10)
-D
If set, classifier is run in debug mode and
may output additional info to the console
-W
Full name of base classifier.
(default: weka.classifiers.trees.J48)
Options specific to classifier weka.classifiers.trees.J48:
-U
Use unpruned tree.
-C <pruning confidence>
Set confidence threshold for pruning.
(default 0.25)
-M <minimum number of instances>
Set minimum number of instances per leaf.
(default 2)
-R
Use reduced error pruning.
-N <number of folds>
Set number of folds for reduced error
pruning. One fold is used as pruning set.
(default 3)
-B
Use binary splits only.
-S
Don't perform subtree raising.
-L
Do not clean up after the tree has been built.
-A
Laplace smoothing for predicted probabilities.
-Q <seed>
Seed for random data shuffling (default 1).