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java.lang.Objectweka.classifiers.Classifier
weka.classifiers.SingleClassifierEnhancer
weka.classifiers.RandomizableSingleClassifierEnhancer
weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
public class DataNearBalancedND
A meta classifier for handling multi-class datasets with 2-class classifiers by building a random data-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.
@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).
| Constructor Summary | |
|---|---|
DataNearBalancedND()
Constructor. |
|
| Method Summary | |
|---|---|
void |
buildClassifier(Instances data)
Builds tree recursively. |
double[] |
distributionForInstance(Instance inst)
Predicts the class distribution for a given instance |
Capabilities |
getCapabilities()
Returns default capabilities of the classifier. |
java.lang.String |
getRevision()
Returns the revision string. |
java.lang.String |
getString(int[] indices)
Returns the list of indices as a string. |
TechnicalInformation |
getTechnicalInformation()
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on. |
java.lang.String |
globalInfo()
|
static void |
main(java.lang.String[] argv)
Main method for testing this class. |
void |
setHashtable(java.util.Hashtable table)
Set hashtable from END. |
java.lang.String |
toString()
Outputs the classifier as a string. |
| Methods inherited from class weka.classifiers.RandomizableSingleClassifierEnhancer |
|---|
getOptions, getSeed, listOptions, seedTipText, setOptions, setSeed |
| Methods inherited from class weka.classifiers.SingleClassifierEnhancer |
|---|
classifierTipText, getClassifier, setClassifier |
| Methods inherited from class weka.classifiers.Classifier |
|---|
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
| Constructor Detail |
|---|
public DataNearBalancedND()
| Method Detail |
|---|
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation in interface TechnicalInformationHandlerpublic void setHashtable(java.util.Hashtable table)
table - the hashtable to usepublic Capabilities getCapabilities()
getCapabilities in interface CapabilitiesHandlergetCapabilities in class SingleClassifierEnhancerCapabilities
public void buildClassifier(Instances data)
throws java.lang.Exception
buildClassifier in class Classifierdata - contains the (multi-class) instances
java.lang.Exception - if the building fails
public double[] distributionForInstance(Instance inst)
throws java.lang.Exception
distributionForInstance in class Classifierinst - the (multi-class) instance to be classified
java.lang.Exception - if computing failspublic java.lang.String getString(int[] indices)
indices - the indices to return as string
public java.lang.String globalInfo()
public java.lang.String toString()
toString in class java.lang.Objectpublic java.lang.String getRevision()
getRevision in interface RevisionHandlergetRevision in class Classifierpublic static void main(java.lang.String[] argv)
argv - the options
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