All Packages Class Hierarchy This Package Previous Next Index WEKA's home
Class weka.classifiers.kstar.KStar
java.lang.Object
|
+----weka.classifiers.Classifier
|
+----weka.classifiers.DistributionClassifier
|
+----weka.classifiers.kstar.KStar
- public class KStar
- extends DistributionClassifier
- implements KStarConstants, OptionHandler, UpdateableClassifier, WeightedInstancesHandler
K* is an instance-based classifier, that is the class of a test
instance is based upon the class of those training instances
similar to it, as determined by some similarity function. The
underlying assumption of instance-based classifiers such as K*,
IB1, PEBLS, etc, is that similar instances will have similar
classes.
For more information on K*, see
John, G. Cleary and Leonard, E. Trigg (1995) "K*: An Instance-
based Learner Using an Entropic Distance Measure",
Proceedings of the 12th International Conference on Machine
learning, pp. 108-114.
- Version:
- $Revision 1.0 $
- Author:
- Len Trigg (len@intelligenesis.net)
- Author:
- Abdelaziz Mahoui (am14@cs.waikato.ac.nz)
TAGS_MISSING- Define possible missing value handling methods
KStar()
-
buildClassifier(Instances)
- Generates the classifier.
distributionForInstance(Instance)
- Calculates the class membership probabilities for the given test instance.
getEntropicAutoBlend()
- Get whether entropic blending being used
getGlobalBlend()
- Get the value of the global blend parameter
getMissingMode()
- Gets the method to use for handling missing values.
getOptions()
- Gets the current settings of K*.
listOptions()
- Returns an enumeration describing the available options
main(String[])
- Main method for testing this class.
setEntropicAutoBlend(boolean)
- Set whether entropic blending is to be used.
setGlobalBlend(int)
- Set the global blend parameter
setMissingMode(SelectedTag)
- Sets the method to use for handling missing values.
setOptions(String[])
- Parses a given list of options.
toString()
- Returns a description of this classifier.
updateClassifier(Instance)
- Adds the supplied instance to the training set
TAGS_MISSING
public static final Tag[] TAGS_MISSING
Define possible missing value handling methods
KStar
public KStar()
buildClassifier
public void buildClassifier(Instances instances) throws java.lang.Exception
Generates the classifier.
- Parameters:
instances
- set of instances serving as training data
- Throws:
- java.lang.Exception - if the classifier has not been generated successfully
- Overrides:
- buildClassifier in class Classifier
updateClassifier
public void updateClassifier(Instance instance) throws java.lang.Exception
Adds the supplied instance to the training set
- Parameters:
instance
- the instance to add
- Throws:
- java.lang.Exception - if instance could not be incorporated successfully
distributionForInstance
public double[] distributionForInstance(Instance instance) throws java.lang.Exception
Calculates the class membership probabilities for the given test instance.
- Parameters:
instance
- the instance to be classified
- Returns:
- predicted class probability distribution
- Throws:
- java.lang.Exception - if an error occurred during the prediction
- Overrides:
- distributionForInstance in class DistributionClassifier
getMissingMode
public SelectedTag getMissingMode()
Gets the method to use for handling missing values. Will be one of
M_NORMAL, M_AVERAGE, M_MAXDIFF or M_DELETE.
- Returns:
- the method used for handling missing values.
setMissingMode
public void setMissingMode(SelectedTag newMode)
Sets the method to use for handling missing values. Values other than
M_NORMAL, M_AVERAGE, M_MAXDIFF and M_DELETE will be ignored.
- Parameters:
newMode
- the method to use for handling missing values.
listOptions
public java.util.Enumeration listOptions()
Returns an enumeration describing the available options
- Returns:
- an enumeration of all the available options
setGlobalBlend
public void setGlobalBlend(int b)
Set the global blend parameter
- Parameters:
b
- the value for global blending
getGlobalBlend
public int getGlobalBlend()
Get the value of the global blend parameter
- Returns:
- the value of the global blend parameter
setEntropicAutoBlend
public void setEntropicAutoBlend(boolean e)
Set whether entropic blending is to be used.
- Parameters:
e
- true if entropic blending is to be used
getEntropicAutoBlend
public boolean getEntropicAutoBlend()
Get whether entropic blending being used
- Returns:
- true if entropic blending is used
setOptions
public void setOptions(java.lang.String options[]) throws java.lang.Exception
Parses a given list of options. Valid options are:
...
- Parameters:
options
- the list of options as an array of strings
- Throws:
- java.lang.Exception - if an option is not supported
getOptions
public java.lang.String[] getOptions()
Gets the current settings of K*.
- Returns:
- an array of strings suitable for passing to setOptions()
toString
public java.lang.String toString()
Returns a description of this classifier.
- Returns:
- a description of this classifier as a string.
- Overrides:
- toString in class java.lang.Object
main
public static void main(java.lang.String argv[])
Main method for testing this class.
- Parameters:
argv
- should contain command line options (see setOptions)
All Packages Class Hierarchy This Package Previous Next Index WEKA's home