Package weka.classifiers.meta
Class InputSmearing
- java.lang.Object
-
- weka.classifiers.AbstractClassifier
-
- weka.classifiers.SingleClassifierEnhancer
-
- weka.classifiers.IteratedSingleClassifierEnhancer
-
- weka.classifiers.ParallelIteratedSingleClassifierEnhancer
-
- weka.classifiers.RandomizableParallelIteratedSingleClassifierEnhancer
-
- weka.classifiers.meta.Bagging
-
- weka.classifiers.meta.InputSmearing
-
- All Implemented Interfaces:
Serializable
,Cloneable
,weka.classifiers.Classifier
,weka.core.AdditionalMeasureProducer
,weka.core.Aggregateable<weka.classifiers.meta.Bagging>
,weka.core.BatchPredictor
,weka.core.CapabilitiesHandler
,weka.core.CapabilitiesIgnorer
,weka.core.CommandlineRunnable
,weka.core.OptionHandler
,weka.core.PartitionGenerator
,weka.core.Randomizable
,weka.core.RevisionHandler
,weka.core.TechnicalInformationHandler
,weka.core.WeightedInstancesHandler
public class InputSmearing extends weka.classifiers.meta.Bagging
Extended version of weka.classifiers.meta.Bagging, which allows input smearing of numeric attributes.
Class for bagging a classifier to reduce variance. Can do classification and regression depending on the base learner.
For more information, see
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
Frank, Eibe, Pfahringer, Bernhard: Improving on Bagging with Input Smearing. In Ng, Wee-Keong and Kitsuregawa, Masaru and Li, Jianzhong and Chang, Kuiyu, editors, Advances in Knowledge Discovery and Data Mining, 97-106, 2006. BibTeX:@article{Breiman1996, author = {Leo Breiman}, journal = {Machine Learning}, number = {2}, pages = {123-140}, title = {Bagging predictors}, volume = {24}, year = {1996} } @incollection{Frank2006, author = {Frank, Eibe and Pfahringer, Bernhard}, booktitle = {Advances in Knowledge Discovery and Data Mining}, editor = {Ng, Wee-Keong and Kitsuregawa, Masaru and Li, Jianzhong and Chang, Kuiyu}, pages = {97-106}, publisher = {Springer Berlin Heidelberg}, series = {Lecture Notes in Computer Science}, title = {Improving on Bagging with Input Smearing}, volume = {3918}, year = {2006}, ISBN = {978-3-540-33206-0}, URL = {http://dx.doi.org/10.1007/11731139_14} }
Valid options are:-stddev <number> The multiplier for the standard deviation of a numeric attribute to use for performing the smearing (default 1.0)
-P Size of each bag, as a percentage of the training set size. (default 100)
-O Calculate the out of bag error.
-represent-copies-using-weights Represent copies of instances using weights rather than explicitly.
-S <num> Random number seed. (default 1)
-num-slots <num> Number of execution slots. (default 1 - i.e. no parallelism) (use 0 to auto-detect number of cores)
-I <num> Number of iterations. (default 10)
-W Full name of base classifier. (default: weka.classifiers.trees.REPTree)
-output-debug-info If set, classifier is run in debug mode and may output additional info to the console
-do-not-check-capabilities If set, classifier capabilities are not checked before classifier is built (use with caution).
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)
-I Initial class value count (default 0)
-R Spread initial count over all class values (i.e. don't use 1 per value)
-output-debug-info If set, classifier is run in debug mode and may output additional info to the console
-do-not-check-capabilities If set, classifier capabilities are not checked before classifier is built (use with caution).
Options after -- are passed to the designated classifier.- Version:
- $Revision$
- Author:
- FracPete (fracpete at waikato dot ac dot nz), Eibe Frank (eibe at waikato dot ac dot nz)
- See Also:
- Serialized Form
-
-
Field Summary
Fields Modifier and Type Field Description protected double
m_StdDev
the standard deviation multiplier to use.-
Fields inherited from class weka.classifiers.meta.Bagging
m_BagSizePercent, m_CalcOutOfBag, m_classifiersCache, m_data, m_inBag, m_OutOfBagEvaluationObject, m_random, m_RepresentUsingWeights
-
Fields inherited from class weka.classifiers.RandomizableParallelIteratedSingleClassifierEnhancer
m_Seed
-
Fields inherited from class weka.classifiers.ParallelIteratedSingleClassifierEnhancer
m_numExecutionSlots
-
-
Constructor Summary
Constructors Constructor Description InputSmearing()
-
Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description String[]
getOptions()
Gets the current settings of the Classifier.String
getRevision()
Returns the revision string.double
getStdDev()
Gets the multiplier for the standard deviation to use for input smearing.weka.core.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.protected weka.core.Instances
getTrainingSet(int iteration)
Returns a training set for a particular iteration.String
globalInfo()
Returns a string describing classifier.Enumeration<weka.core.Option>
listOptions()
Returns an enumeration describing the available options.static void
main(String[] args)
Main method for testing this class.void
setOptions(String[] options)
Parses a given list of options.void
setStdDev(double value)
Sets the multiplier for the standard deviation to use for input smearing.String
stdDevTipText()
Returns the tip text for this property-
Methods inherited from class weka.classifiers.meta.Bagging
aggregate, bagSizePercentTipText, batchSizeTipText, buildClassifier, calcOutOfBagTipText, defaultClassifierString, distributionForInstance, distributionsForInstances, enumerateMeasures, finalizeAggregation, generatePartition, getBagSizePercent, getBatchSize, getCalcOutOfBag, getMeasure, getMembershipValues, getOutOfBagEvaluationObject, getOutputOutOfBagComplexityStatistics, getPrintClassifiers, getRepresentCopiesUsingWeights, getStoreOutOfBagPredictions, implementsMoreEfficientBatchPrediction, measureOutOfBagError, numElements, outputOutOfBagComplexityStatisticsTipText, printClassifiersTipText, representCopiesUsingWeightsTipText, setBagSizePercent, setBatchSize, setCalcOutOfBag, setOutputOutOfBagComplexityStatistics, setPrintClassifiers, setRepresentCopiesUsingWeights, setStoreOutOfBagPredictions, storeOutOfBagPredictionsTipText, toString
-
Methods inherited from class weka.classifiers.RandomizableParallelIteratedSingleClassifierEnhancer
getSeed, seedTipText, setSeed
-
Methods inherited from class weka.classifiers.ParallelIteratedSingleClassifierEnhancer
buildClassifiers, getNumExecutionSlots, numExecutionSlotsTipText, setNumExecutionSlots
-
Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer
defaultNumberOfIterations, getNumIterations, numIterationsTipText, setNumIterations
-
Methods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, defaultClassifierOptions, getCapabilities, getClassifier, getClassifierSpec, postExecution, preExecution, setClassifier
-
Methods inherited from class weka.classifiers.AbstractClassifier
classifyInstance, debugTipText, doNotCheckCapabilitiesTipText, forName, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces
-
-
-
-
Method Detail
-
globalInfo
public String globalInfo()
Returns a string describing classifier.- Overrides:
globalInfo
in classweka.classifiers.meta.Bagging
- Returns:
- a description suitable for displaying in the explorer/experimenter gui
-
getTechnicalInformation
public weka.core.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.- Specified by:
getTechnicalInformation
in interfaceweka.core.TechnicalInformationHandler
- Overrides:
getTechnicalInformation
in classweka.classifiers.meta.Bagging
- Returns:
- the technical information about this class
-
listOptions
public Enumeration<weka.core.Option> listOptions()
Returns an enumeration describing the available options.- Specified by:
listOptions
in interfaceweka.core.OptionHandler
- Overrides:
listOptions
in classweka.classifiers.meta.Bagging
- Returns:
- an enumeration of all the available options.
-
setOptions
public void setOptions(String[] options) throws Exception
Parses a given list of options.- Specified by:
setOptions
in interfaceweka.core.OptionHandler
- Overrides:
setOptions
in classweka.classifiers.meta.Bagging
- Parameters:
options
- the list of options as an array of strings- Throws:
Exception
- if an option is not supported
-
getOptions
public String[] getOptions()
Gets the current settings of the Classifier.- Specified by:
getOptions
in interfaceweka.core.OptionHandler
- Overrides:
getOptions
in classweka.classifiers.meta.Bagging
- Returns:
- an array of strings suitable for passing to setOptions
-
getStdDev
public double getStdDev()
Gets the multiplier for the standard deviation to use for input smearing.- Returns:
- the multiplier
-
setStdDev
public void setStdDev(double value)
Sets the multiplier for the standard deviation to use for input smearing.- Parameters:
value
- the multiplier
-
stdDevTipText
public String stdDevTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
-
getTrainingSet
protected weka.core.Instances getTrainingSet(int iteration) throws Exception
Returns a training set for a particular iteration.- Overrides:
getTrainingSet
in classweka.classifiers.meta.Bagging
- Parameters:
iteration
- the number of the iteration for the requested training set.- Returns:
- the training set for the supplied iteration number
- Throws:
Exception
- if something goes wrong when generating a training set.
-
getRevision
public String getRevision()
Returns the revision string.- Specified by:
getRevision
in interfaceweka.core.RevisionHandler
- Overrides:
getRevision
in classweka.classifiers.meta.Bagging
- Returns:
- the revision
-
main
public static void main(String[] args)
Main method for testing this class.- Parameters:
args
- the options
-
-