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java.lang.Objectweka.classifiers.Classifier
weka.classifiers.SingleClassifierEnhancer
weka.classifiers.mi.MIWrapper
public class MIWrapper
A simple Wrapper method for applying standard propositional learners to multi-instance data.
For more information see:
E. T. Frank, X. Xu (2003). Applying propositional learning algorithms to multi-instance data. Department of Computer Science, University of Waikato, Hamilton, NZ.
@techreport{Frank2003, address = {Department of Computer Science, University of Waikato, Hamilton, NZ}, author = {E. T. Frank and X. Xu}, institution = {University of Waikato}, month = {06}, title = {Applying propositional learning algorithms to multi-instance data}, year = {2003} }Valid options are:
-P [1|2|3] The method used in testing: 1.arithmetic average 2.geometric average 3.max probability of positive bag. (default: 1)
-A [0|1|2|3] The type of weight setting for each single-instance: 0.keep the weight to be the same as the original value; 1.weight = 1.0 2.weight = 1.0/Total number of single-instance in the corresponding bag 3. weight = Total number of single-instance / (Total number of bags * Total number of single-instance in the corresponding bag). (default: 3)
-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 console
Field Summary | |
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static Tag[] |
TAGS_TESTMETHOD
the test methods |
static int |
TESTMETHOD_ARITHMETIC
arithmetic average |
static int |
TESTMETHOD_GEOMETRIC
geometric average |
static int |
TESTMETHOD_MAXPROB
max probability of positive bag |
Constructor Summary | |
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MIWrapper()
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Method Summary | |
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void |
buildClassifier(Instances data)
Builds the classifier |
double[] |
distributionForInstance(Instance exmp)
Computes the distribution for a given exemplar |
Capabilities |
getCapabilities()
Returns default capabilities of the classifier. |
SelectedTag |
getMethod()
Get the method used in testing. |
Capabilities |
getMultiInstanceCapabilities()
Returns the capabilities of this multi-instance classifier for the relational data. |
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. |
SelectedTag |
getWeightMethod()
Returns the current weighting method for instances. |
java.lang.String |
globalInfo()
Returns a string describing this filter |
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 |
methodTipText()
Returns the tip text for this property |
void |
setMethod(SelectedTag method)
Set the method used in testing. |
void |
setOptions(java.lang.String[] options)
Parses a given list of options. |
void |
setWeightMethod(SelectedTag method)
The new method for weighting the instances. |
java.lang.String |
toString()
Gets a string describing the classifier. |
java.lang.String |
weightMethodTipText()
Returns the tip text for this property |
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 TESTMETHOD_ARITHMETIC
public static final int TESTMETHOD_GEOMETRIC
public static final int TESTMETHOD_MAXPROB
public static final Tag[] TAGS_TESTMETHOD
Constructor Detail |
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public MIWrapper()
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 SingleClassifierEnhancer
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-P [1|2|3] The method used in testing: 1.arithmetic average 2.geometric average 3.max probability of positive bag. (default: 1)
-A [0|1|2|3] The type of weight setting for each single-instance: 0.keep the weight to be the same as the original value; 1.weight = 1.0 2.weight = 1.0/Total number of single-instance in the corresponding bag 3. weight = Total number of single-instance / (Total number of bags * Total number of single-instance in the corresponding bag). (default: 3)
-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 console
setOptions
in interface OptionHandler
setOptions
in class SingleClassifierEnhancer
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 SingleClassifierEnhancer
public java.lang.String weightMethodTipText()
public void setWeightMethod(SelectedTag method)
method
- the new methodpublic SelectedTag getWeightMethod()
public java.lang.String methodTipText()
public void setMethod(SelectedTag method)
method
- the index of method to use.public SelectedTag getMethod()
public Capabilities getCapabilities()
getCapabilities
in interface CapabilitiesHandler
getCapabilities
in class SingleClassifierEnhancer
Capabilities
public Capabilities getMultiInstanceCapabilities()
getMultiInstanceCapabilities
in interface MultiInstanceCapabilitiesHandler
Capabilities
public void buildClassifier(Instances data) throws java.lang.Exception
buildClassifier
in class Classifier
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 exmp) throws java.lang.Exception
distributionForInstance
in class Classifier
exmp
- the exemplar for which distribution is computed
java.lang.Exception
- if the distribution can't be computed successfullypublic java.lang.String toString()
toString
in class java.lang.Object
public java.lang.String getRevision()
getRevision
in interface RevisionHandler
public static void main(java.lang.String[] argv)
argv
- should contain the command line arguments to the
scheme (see Evaluation)
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