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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 classifier using Freund & Schapire's Adaboost M1 method. For more information, see
Yoav Freund and Robert E. Schapire (1996). Experiments with a new boosting algorithm. Proc International Conference on Machine Learning, pages 148-156, Morgan Kaufmann, San Francisco.
Valid options are:
-D
Turn on debugging output.
-W classname
Specify the full class name of a classifier as the basis for
boosting (required).
-I num
Set the number of boost iterations (default 10).
-P num
Set the percentage of weight mass used to build classifiers
(default 100).
-Q
Use resampling instead of reweighting.
-S seed
Random number seed for resampling (default 1).
Options 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. |
java.lang.String[] |
getOptions()
Gets the current settings of the Classifier. |
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 java.util.Enumeration listOptions()
listOptions
in interface OptionHandler
listOptions
in class RandomizableIteratedSingleClassifierEnhancer
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-D
Turn on debugging output.
-W classname
Specify the full class name of a classifier as the basis for
boosting (required).
-I num
Set the number of boost iterations (default 10).
-P num
Set the percentage of weight mass used to build classifiers
(default 100).
-Q
Use resampling instead of reweighting.
-S seed
Random number seed for resampling (default 1).
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 weightThresholdTipText()
public void setWeightThreshold(int threshold)
thresholding
- the percentage of weight mass used for trainingpublic int getWeightThreshold()
public java.lang.String useResamplingTipText()
public void setUseResampling(boolean r)
resampling
- true if resampling should be donepublic boolean getUseResampling()
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 name that should be given to the source class.
java.lang.Exception
- if something goes wrongpublic java.lang.String toString()
toString
in class java.lang.Object
public static void main(java.lang.String[] argv)
argv
- the options
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