A meta classifier for handling multi-class datasets with 2-class classifiers by building a random class-balanced tree structure.
For more info, check
Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems. In: PKDD, 84-95, 2005.
Eibe Frank, Stefan Kramer: Ensembles of nested dichotomies for multi-class problems. In: Twenty-first International Conference on Machine Learning, 2004.
BibTeX:
@inproceedings{Dong2005,
author = {Lin Dong and Eibe Frank and Stefan Kramer},
booktitle = {PKDD},
pages = {84-95},
publisher = {Springer},
title = {Ensembles of Balanced Nested Dichotomies for Multi-class Problems},
year = {2005}
}
@inproceedings{Frank2004,
author = {Eibe Frank and Stefan Kramer},
booktitle = {Twenty-first International Conference on Machine Learning},
publisher = {ACM},
title = {Ensembles of nested dichotomies for multi-class problems},
year = {2004}
}
Valid options are:
-S <num>
Random number seed.
(default 1)
-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).
author:
Lin Dong
author:
Eibe Frank | 