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java.lang.Objectweka.classifiers.Classifier
weka.classifiers.SingleClassifierEnhancer
weka.classifiers.IteratedSingleClassifierEnhancer
weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
weka.classifiers.meta.AdaBoostM1
public class AdaBoostM1
Class for boosting a nominal class classifier using the Adaboost M1 method. Only nominal class problems can be tackled. Often dramatically improves performance, but sometimes overfits.
For more information, see
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
@inproceedings{Freund1996, address = {San Francisco}, author = {Yoav Freund and Robert E. Schapire}, booktitle = {Thirteenth International Conference on Machine Learning}, pages = {148-156}, publisher = {Morgan Kaufmann}, title = {Experiments with a new boosting algorithm}, year = {1996} }Valid options are:
-P <num> Percentage of weight mass to base training on. (default 100, reduce to around 90 speed up)
-Q Use resampling for boosting.
-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.DecisionStump)
Options specific to classifier weka.classifiers.trees.DecisionStump:
-D If set, classifier is run in debug mode and may output additional info to the consoleOptions after -- are passed to the designated classifier.
Constructor Summary | |
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AdaBoostM1()
Constructor. |
Method Summary | |
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void |
buildClassifier(Instances data)
Boosting method. |
double[] |
distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
Capabilities |
getCapabilities()
Returns default capabilities of the classifier. |
java.lang.String[] |
getOptions()
Gets the current settings of the Classifier. |
java.lang.String |
getRevision()
Returns the revision 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. |
boolean |
getUseResampling()
Get whether resampling is turned on |
int |
getWeightThreshold()
Get the degree of weight thresholding |
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[] argv)
Main method for testing this class. |
void |
setOptions(java.lang.String[] options)
Parses a given list of options. |
void |
setUseResampling(boolean r)
Set resampling mode |
void |
setWeightThreshold(int threshold)
Set weight threshold |
java.lang.String |
toSource(java.lang.String className)
Returns the boosted model as Java source code. |
java.lang.String |
toString()
Returns description of the boosted classifier. |
java.lang.String |
useResamplingTipText()
Returns the tip text for this property |
java.lang.String |
weightThresholdTipText()
Returns the tip text for this property |
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, 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 AdaBoostM1()
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 <num> Percentage of weight mass to base training on. (default 100, reduce to around 90 speed up)
-Q Use resampling for boosting.
-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.DecisionStump)
Options specific to classifier weka.classifiers.trees.DecisionStump:
-D If set, classifier is run in debug mode and may output additional info to the consoleOptions 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 weightThresholdTipText()
public void setWeightThreshold(int threshold)
threshold
- the percentage of weight mass used for trainingpublic int getWeightThreshold()
public java.lang.String useResamplingTipText()
public void setUseResampling(boolean r)
r
- true if resampling should be donepublic boolean getUseResampling()
public Capabilities getCapabilities()
getCapabilities
in interface CapabilitiesHandler
getCapabilities
in class SingleClassifierEnhancer
Capabilities
public void buildClassifier(Instances data) throws java.lang.Exception
buildClassifier
in class IteratedSingleClassifierEnhancer
data
- the training data to be used for generating the
boosted 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 instance could not be classified
successfullypublic java.lang.String toSource(java.lang.String className) throws java.lang.Exception
toSource
in interface Sourcable
className
- the classname of the generated class
java.lang.Exception
- if something goes wrongpublic java.lang.String toString()
toString
in class java.lang.Object
public java.lang.String getRevision()
getRevision
in interface RevisionHandler
getRevision
in class Classifier
public static void main(java.lang.String[] argv)
argv
- the options
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