类 RandomSubSpace
- 所有已实现的接口:
Serializable
,Cloneable
,CapabilitiesHandler
,OptionHandler
,Randomizable
,RevisionHandler
,TechnicalInformationHandler
,WeightedInstancesHandler
public class RandomSubSpace
extends RandomizableIteratedSingleClassifierEnhancer
implements WeightedInstancesHandler, TechnicalInformationHandler
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. BibTeX:
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. BibTeX:
@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.
- 版本:
- $Revision: 1.4 $
- 作者:
- Bernhard Pfahringer (bernhard@cs.waikato.ac.nz), Peter Reutemann (fracpete@cs.waikato.ac.nz)
- 另请参阅:
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构造器概要
构造器 -
方法概要
修饰符和类型方法说明void
buildClassifier
(Instances data) builds the classifier.double[]
distributionForInstance
(Instance instance) Calculates the class membership probabilities for the given test instance.String[]
Gets the current settings of the Classifier.Returns the revision string.double
Gets the size of each subSpace, as a percentage of the training set size.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.Returns a string describing classifierReturns an enumeration describing the available options.static void
Main method for testing this class.void
setOptions
(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.Returns the tip text for this propertytoString()
Returns description of the bagged classifier.从类继承的方法 weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
getSeed, seedTipText, setSeed
从类继承的方法 weka.classifiers.IteratedSingleClassifierEnhancer
getNumIterations, numIterationsTipText, setNumIterations
从类继承的方法 weka.classifiers.SingleClassifierEnhancer
classifierTipText, getCapabilities, getClassifier, setClassifier
从类继承的方法 weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug
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构造器详细资料
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RandomSubSpace
public RandomSubSpace()Constructor.
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方法详细资料
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globalInfo
Returns a string describing classifier- 返回:
- a description suitable for displaying in the explorer/experimenter gui
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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.- 指定者:
getTechnicalInformation
在接口中TechnicalInformationHandler
- 返回:
- the technical information about this class
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listOptions
Returns an enumeration describing the available options.- 指定者:
listOptions
在接口中OptionHandler
- 覆盖:
listOptions
在类中RandomizableIteratedSingleClassifierEnhancer
- 返回:
- an enumeration of all the available options.
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setOptions
Parses a given list of options. 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.- 指定者:
setOptions
在接口中OptionHandler
- 覆盖:
setOptions
在类中RandomizableIteratedSingleClassifierEnhancer
- 参数:
options
- the list of options as an array of strings- 抛出:
Exception
- if an option is not supported
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getOptions
Gets the current settings of the Classifier.- 指定者:
getOptions
在接口中OptionHandler
- 覆盖:
getOptions
在类中RandomizableIteratedSingleClassifierEnhancer
- 返回:
- an array of strings suitable for passing to setOptions
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subSpaceSizeTipText
Returns the tip text for this property- 返回:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getSubSpaceSize
public double getSubSpaceSize()Gets the size of each subSpace, as a percentage of the training set size.- 返回:
- the subSpace size, as a percentage.
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setSubSpaceSize
public void setSubSpaceSize(double value) Sets the size of each subSpace, as a percentage of the training set size.- 参数:
value
- the subSpace size, as a percentage.
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buildClassifier
builds the classifier.- 覆盖:
buildClassifier
在类中IteratedSingleClassifierEnhancer
- 参数:
data
- the training data to be used for generating the classifier.- 抛出:
Exception
- if the classifier could not be built successfully
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distributionForInstance
Calculates the class membership probabilities for the given test instance.- 覆盖:
distributionForInstance
在类中Classifier
- 参数:
instance
- the instance to be classified- 返回:
- preedicted class probability distribution
- 抛出:
Exception
- if distribution can't be computed successfully
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toString
Returns description of the bagged classifier. -
getRevision
Returns the revision string.- 指定者:
getRevision
在接口中RevisionHandler
- 覆盖:
getRevision
在类中Classifier
- 返回:
- the revision
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main
Main method for testing this class.- 参数:
args
- the options
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