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java.lang.Objectweka.classifiers.Classifier
weka.classifiers.SingleClassifierEnhancer
weka.classifiers.IteratedSingleClassifierEnhancer
weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
weka.classifiers.meta.RandomSubSpace
public class RandomSubSpace
This method constructs a decision tree based classifier that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity. The classifier consists of multiple trees constructed systematically by pseudorandomly selecting subsets of components of the feature vector, that is, trees constructed in randomly chosen subspaces.
For more information, see
Tin Kam Ho (1998). The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20(8):832-844. URL http://citeseer.ist.psu.edu/ho98random.html.
@article{Ho1998, author = {Tin Kam Ho}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, number = {8}, pages = {832-844}, title = {The Random Subspace Method for Constructing Decision Forests}, volume = {20}, year = {1998}, ISSN = {0162-8828}, URL = {http://citeseer.ist.psu.edu/ho98random.html} }Valid options are:
-P Size of each subspace: < 1: percentage of the number of attributes >=1: absolute number of attributes
-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.REPTree)
Options specific to classifier weka.classifiers.trees.REPTree:
-M <minimum number of instances> Set minimum number of instances per leaf (default 2).
-V <minimum variance for split> Set minimum numeric class variance proportion of train variance for split (default 1e-3).
-N <number of folds> Number of folds for reduced error pruning (default 3).
-S <seed> Seed for random data shuffling (default 1).
-P No pruning.
-L Maximum tree depth (default -1, no maximum)Options after -- are passed to the designated classifier.
Constructor Summary | |
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RandomSubSpace()
Constructor. |
Method Summary | |
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void |
buildClassifier(Instances data)
builds the classifier. |
double[] |
distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
java.lang.String[] |
getOptions()
Gets the current settings of the Classifier. |
java.lang.String |
getRevision()
Returns the revision string. |
double |
getSubSpaceSize()
Gets the size of each subSpace, as a percentage of the training set size. |
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()
Returns a string describing classifier |
java.util.Enumeration |
listOptions()
Returns an enumeration describing the available options. |
static void |
main(java.lang.String[] args)
Main method for testing this class. |
void |
setOptions(java.lang.String[] options)
Parses a given list of options. |
void |
setSubSpaceSize(double value)
Sets the size of each subSpace, as a percentage of the training set size. |
java.lang.String |
subSpaceSizeTipText()
Returns the tip text for this property |
java.lang.String |
toString()
Returns description of the bagged classifier. |
Methods inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer |
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getSeed, seedTipText, setSeed |
Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer |
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getNumIterations, numIterationsTipText, setNumIterations |
Methods inherited from class weka.classifiers.SingleClassifierEnhancer |
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classifierTipText, getCapabilities, getClassifier, setClassifier |
Methods inherited from class weka.classifiers.Classifier |
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classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Constructor Detail |
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public RandomSubSpace()
Method Detail |
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public java.lang.String globalInfo()
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation
in interface TechnicalInformationHandler
public java.util.Enumeration listOptions()
listOptions
in interface OptionHandler
listOptions
in class RandomizableIteratedSingleClassifierEnhancer
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-P Size of each subspace: < 1: percentage of the number of attributes >=1: absolute number of attributes
-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.REPTree)
Options specific to classifier weka.classifiers.trees.REPTree:
-M <minimum number of instances> Set minimum number of instances per leaf (default 2).
-V <minimum variance for split> Set minimum numeric class variance proportion of train variance for split (default 1e-3).
-N <number of folds> Number of folds for reduced error pruning (default 3).
-S <seed> Seed for random data shuffling (default 1).
-P No pruning.
-L Maximum tree depth (default -1, no maximum)Options after -- are passed to the designated classifier.
setOptions
in interface OptionHandler
setOptions
in class RandomizableIteratedSingleClassifierEnhancer
options
- the list of options as an array of strings
java.lang.Exception
- if an option is not supportedpublic java.lang.String[] getOptions()
getOptions
in interface OptionHandler
getOptions
in class RandomizableIteratedSingleClassifierEnhancer
public java.lang.String subSpaceSizeTipText()
public double getSubSpaceSize()
public void setSubSpaceSize(double value)
value
- the subSpace size, as a percentage.public void buildClassifier(Instances data) throws java.lang.Exception
buildClassifier
in class IteratedSingleClassifierEnhancer
data
- the training data to be used for generating the
classifier.
java.lang.Exception
- if the classifier could not be built successfullypublic double[] distributionForInstance(Instance instance) throws java.lang.Exception
distributionForInstance
in class Classifier
instance
- the instance to be classified
java.lang.Exception
- if distribution can't be computed successfullypublic java.lang.String toString()
toString
in class java.lang.Object
public java.lang.String getRevision()
getRevision
in interface RevisionHandler
public static void main(java.lang.String[] args)
args
- the options
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