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java.lang.Objectweka.classifiers.Classifier
weka.classifiers.SingleClassifierEnhancer
weka.classifiers.RandomizableSingleClassifierEnhancer
weka.classifiers.meta.CostSensitiveClassifier
public class CostSensitiveClassifier
A metaclassifier that makes its base classifier cost-sensitive. Two methods can be used to introduce cost-sensitivity: reweighting training instances according to the total cost assigned to each class; or predicting the class with minimum expected misclassification cost (rather than the most likely class). Performance can often be improved by using a Bagged classifier to improve the probability estimates of the base classifier.
Valid options are:-M Minimize expected misclassification cost. Default is to reweight training instances according to costs per class
-C <cost file name> File name of a cost matrix to use. If this is not supplied, a cost matrix will be loaded on demand. The name of the on-demand file is the relation name of the training data plus ".cost", and the path to the on-demand file is specified with the -N option.
-N <directory> Name of a directory to search for cost files when loading costs on demand (default current directory).
-cost-matrix <matrix> The cost matrix in Matlab single line format.
-S <num> Random number seed. (default 1)
-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.rules.ZeroR)
Options specific to classifier weka.classifiers.rules.ZeroR:
-D If set, classifier is run in debug mode and may output additional info to the consoleOptions after -- are passed to the designated classifier.
Field Summary | |
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static int |
MATRIX_ON_DEMAND
load cost matrix on demand |
static int |
MATRIX_SUPPLIED
use explicit cost matrix |
static Tag[] |
TAGS_MATRIX_SOURCE
Specify possible sources of the cost matrix |
Fields inherited from interface weka.core.Drawable |
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BayesNet, Newick, NOT_DRAWABLE, TREE |
Constructor Summary | |
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CostSensitiveClassifier()
Default constructor. |
Method Summary | |
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void |
buildClassifier(Instances data)
Builds the model of the base learner. |
java.lang.String |
costMatrixSourceTipText()
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java.lang.String |
costMatrixTipText()
|
double[] |
distributionForInstance(Instance instance)
Returns class probabilities. |
Capabilities |
getCapabilities()
Returns default capabilities of the classifier. |
CostMatrix |
getCostMatrix()
Gets the misclassification cost matrix. |
SelectedTag |
getCostMatrixSource()
Gets the source location method of the cost matrix. |
boolean |
getMinimizeExpectedCost()
Gets the value of MinimizeExpectedCost. |
java.io.File |
getOnDemandDirectory()
Returns the directory that will be searched for cost files when loading on demand. |
java.lang.String[] |
getOptions()
Gets the current settings of the Classifier. |
java.lang.String |
getRevision()
Returns the revision string. |
java.lang.String |
globalInfo()
|
java.lang.String |
graph()
Returns graph describing the classifier (if possible). |
int |
graphType()
Returns the type of graph this classifier represents. |
java.util.Enumeration |
listOptions()
Returns an enumeration describing the available options. |
static void |
main(java.lang.String[] argv)
Main method for testing this class. |
java.lang.String |
minimizeExpectedCostTipText()
|
java.lang.String |
onDemandDirectoryTipText()
|
void |
setCostMatrix(CostMatrix newCostMatrix)
Sets the misclassification cost matrix. |
void |
setCostMatrixSource(SelectedTag newMethod)
Sets the source location of the cost matrix. |
void |
setMinimizeExpectedCost(boolean newMinimizeExpectedCost)
Set the value of MinimizeExpectedCost. |
void |
setOnDemandDirectory(java.io.File newDir)
Sets the directory that will be searched for cost files when loading on demand. |
void |
setOptions(java.lang.String[] options)
Parses a given list of options. |
java.lang.String |
toString()
Output a representation of this classifier |
Methods inherited from class weka.classifiers.RandomizableSingleClassifierEnhancer |
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getSeed, seedTipText, setSeed |
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 |
Field Detail |
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public static final int MATRIX_ON_DEMAND
public static final int MATRIX_SUPPLIED
public static final Tag[] TAGS_MATRIX_SOURCE
Constructor Detail |
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public CostSensitiveClassifier()
Method Detail |
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public java.util.Enumeration listOptions()
listOptions
in interface OptionHandler
listOptions
in class RandomizableSingleClassifierEnhancer
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-M Minimize expected misclassification cost. Default is to reweight training instances according to costs per class
-C <cost file name> File name of a cost matrix to use. If this is not supplied, a cost matrix will be loaded on demand. The name of the on-demand file is the relation name of the training data plus ".cost", and the path to the on-demand file is specified with the -N option.
-N <directory> Name of a directory to search for cost files when loading costs on demand (default current directory).
-cost-matrix <matrix> The cost matrix in Matlab single line format.
-S <num> Random number seed. (default 1)
-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.rules.ZeroR)
Options specific to classifier weka.classifiers.rules.ZeroR:
-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 RandomizableSingleClassifierEnhancer
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 RandomizableSingleClassifierEnhancer
public java.lang.String globalInfo()
public java.lang.String costMatrixSourceTipText()
public SelectedTag getCostMatrixSource()
public void setCostMatrixSource(SelectedTag newMethod)
newMethod
- the cost matrix location method.public java.lang.String onDemandDirectoryTipText()
public java.io.File getOnDemandDirectory()
public void setOnDemandDirectory(java.io.File newDir)
newDir
- The cost file search directory.public java.lang.String minimizeExpectedCostTipText()
public boolean getMinimizeExpectedCost()
public void setMinimizeExpectedCost(boolean newMinimizeExpectedCost)
newMinimizeExpectedCost
- Value to assign to MinimizeExpectedCost.public java.lang.String costMatrixTipText()
public CostMatrix getCostMatrix()
public void setCostMatrix(CostMatrix newCostMatrix)
newCostMatrix
- the cost matrixpublic Capabilities getCapabilities()
getCapabilities
in interface CapabilitiesHandler
getCapabilities
in class SingleClassifierEnhancer
Capabilities
public void buildClassifier(Instances data) throws java.lang.Exception
buildClassifier
in class Classifier
data
- the training data
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 int graphType()
graphType
in interface Drawable
public java.lang.String graph() throws java.lang.Exception
graph
in interface Drawable
java.lang.Exception
- if the classifier cannot be graphedpublic 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
- should contain the following arguments:
-t training file [-T test file] [-c class index]
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