A meta classifier for handling multi-class datasets with 2-class classifiers by building an ensemble of nested dichotomies. 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.
(based on WEKA 3.7)
For further options, click the 'More' - button in the dialog.
All weka dialogs have a panel where you can specify classifier-specific parameters.
- END Options
S: Random number seed.
(default 1)
I: 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.meta.nestedDichotomies.ND)
S: 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)
U: Use unpruned tree.
O: Do not collapse tree.
C: Set confidence threshold for pruning.
(default 0.25)
M: Set minimum number of instances per leaf.
(default 2)
R: Use reduced error pruning.
N: 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.
J: Do not use MDL correction for info gain on numeric attributes.
Q: Seed for random data shuffling (default 1).
- Select target column
- Choose the column that contains the target variable.
- Preliminary Attribute Check
The Preliminary Attribute Check tests the underlying classifier against the DataTable specification at the inport of the node. Columns that are compatible with the classifier are marked with a green 'ok'. Columns which are potentially not compatible are assigned a red error message.
Important: If a column is marked as 'incompatible', it does not necessarily mean that the classifier cannot be executed! Sometimes, the error message 'Cannot handle String class' simply means that no nominal values are available (yet). This may change during execution of the predecessor nodes.
Capabilities: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Date attributes, Missing values, Nominal class, Binary class, Missing class values]
Dependencies: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Date attributes, String attributes, Relational attributes, Missing values, No class, Nominal class, Binary class, Unary class, Empty nominal class, Numeric class, Date class, String class, Relational class, Missing class values, Only multi-Instance data]
min # Instance: 1
- Command line options
It shows the command line options according to the current classifier configuration and mainly serves to support the node's configuration via flow variables.
