java.lang.Object
- weka.classifiers.Classifier
- - weka.classifiers.mi.CitationKNN

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, MultiInstanceCapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler

public class CitationKNN
extends Classifier
implements OptionHandler, MultiInstanceCapabilitiesHandler, TechnicalInformationHandler

Modified version of the Citation kNN multi instance classifier.

For more information see:

Jun Wang, Zucker, Jean-Daniel: Solving Multiple-Instance Problem: A Lazy Learning Approach. In: 17th International Conference on Machine Learning, 1119-1125, 2000.

BibTeX:

 @inproceedings{Wang2000,
    author = {Jun Wang and Zucker and Jean-Daniel},
    booktitle = {17th International Conference on Machine Learning},
    editor = {Pat Langley},
    pages = {1119-1125},
    title = {Solving Multiple-Instance Problem: A Lazy Learning Approach},
    year = {2000}
 }

Valid options are:

 -R <number of references>
  Number of Nearest References (default 1)

 -C <number of citers>
  Number of Nearest Citers (default 1)

 -H <rank>
  Rank of the Hausdorff Distance (default 1)

Version:: $Revision: 9146 $
Author:: Miguel Garcia Torres (mgarciat@ull.es)
See Also:: Serialized Form

Constructor Summary

Constructors
Constructor Description

CitationKNN()

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`void`	`buildClassifier(Instances train)`	Builds the classifier
`void`	`buildCNN()`	generates all the variables associated to the citation classifier
`void`	`countBagCiters(Instance bag)`	calculates the citers associated to a bag
`void`	`countBagReferences(Instance bag)`	Calculates the references of the exemplar bag
`double`	`distance(Instance first, Instance second)`	distance between two instances
`double`	`distanceSet(Instance first, Instance second)`	Calculates the distance between two instances
`double[]`	`distributionForInstance(Instance bag)`	Computes the distribution for a given exemplar
`boolean`	`equalExemplars(Instance exemplar1, Instance exemplar2)`	Wether the instances of two exemplars are or are not equal
`Capabilities`	`getCapabilities()`	Returns default capabilities of the classifier.
`int`	`getHDRank()`	Returns the rank associated to the Hausdorff distance
`Capabilities`	`getMultiInstanceCapabilities()`	Returns the capabilities of this multi-instance classifier for the relational data.
`int`	`getNumCiters()`	Returns the number of citers considered to estimate the class prediction of tests bags
`int`	`getNumReferences()`	Returns the number of references considered to estimate the class prediction of tests bags
`java.lang.String[]`	`getOptions()`	Gets the current option settings for the OptionHandler.
`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.
`java.lang.String`	`globalInfo()`	Returns a string describing this filter
`java.lang.String`	`HDRankTipText()`	Returns the tip text for this property
`java.util.Enumeration`	`listOptions()`	Returns an enumeration of all the available options..
`static void`	`main(java.lang.String[] argv)`	Main method for testing this class.
`java.lang.String`	`numCitersTipText()`	Returns the tip text for this property
`java.lang.String`	`numReferencesTipText()`	Returns the tip text for this property
`void`	`preprocessData()`	Calculates the normalization of each attribute.
`void`	`setHDRank(int hDRank)`	Sets the rank associated to the Hausdorff distance
`void`	`setNumCiters(int numCiters)`	Sets the number of citers considered to estimate the class prediction of tests bags
`void`	`setNumReferences(int numReferences)`	Sets the number of references considered to estimate the class prediction of tests bags
`void`	`setOptions(java.lang.String[] options)`	Sets the OptionHandler's options using the given list.
`java.lang.String`	`toString()`	returns a string representation of the classifier
`void`	`updateNormalization(Instance bag)`	Updates the normalization of each attribute.

Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - CitationKNN
```
public CitationKNN()
```
- Method Detail
  - globalInfo
```
public java.lang.String globalInfo()
```
    Returns a string describing this filter
    
    Returns:
    
    a description of the filter suitable for displaying in the explorer/experimenter gui
  - getTechnicalInformation
```
public 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.
    
    Specified by:
    
    getTechnicalInformation in interface TechnicalInformationHandler
    
    Returns:
    
    the technical information about this class
  - preprocessData
```
public void preprocessData()
```
    Calculates the normalization of each attribute.
  - HDRankTipText
```
public java.lang.String HDRankTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setHDRank
```
public void setHDRank(int hDRank)
```
    Sets the rank associated to the Hausdorff distance
    
    Parameters:
    
    hDRank - the rank of the Hausdorff distance
  - getHDRank
```
public int getHDRank()
```
    Returns the rank associated to the Hausdorff distance
    
    Returns:
    
    the rank number
  - numReferencesTipText
```
public java.lang.String numReferencesTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setNumReferences
```
public void setNumReferences(int numReferences)
```
    Sets the number of references considered to estimate the class prediction of tests bags
    
    Parameters:
    
    numReferences - the number of references
  - getNumReferences
```
public int getNumReferences()
```
    Returns the number of references considered to estimate the class prediction of tests bags
    
    Returns:
    
    the number of references
  - numCitersTipText
```
public java.lang.String numCitersTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setNumCiters
```
public void setNumCiters(int numCiters)
```
    Sets the number of citers considered to estimate the class prediction of tests bags
    
    Parameters:
    
    numCiters - the number of citers
  - getNumCiters
```
public int getNumCiters()
```
    Returns the number of citers considered to estimate the class prediction of tests bags
    
    Returns:
    
    the number of citers
  - getCapabilities
```
public Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class Classifier
    
    Returns:
    
    the capabilities of this classifier
    
    See Also:
    
    Capabilities
  - getMultiInstanceCapabilities
```
public Capabilities getMultiInstanceCapabilities()
```
    Returns the capabilities of this multi-instance classifier for the relational data.
    
    Specified by:
    
    getMultiInstanceCapabilities in interface MultiInstanceCapabilitiesHandler
    
    Returns:
    
    the capabilities of this object
    
    See Also:
    
    Capabilities
  - buildClassifier
```
public void buildClassifier(Instances train)
                     throws java.lang.Exception
```
    Builds the classifier
    
    Specified by:
    
    buildClassifier in class Classifier
    
    Parameters:
    
    train - the training data to be used for generating the boosted classifier.
    
    Throws:
    
    java.lang.Exception - if the classifier could not be built successfully
  - buildCNN
```
public void buildCNN()
              throws java.lang.Exception
```
    generates all the variables associated to the citation classifier
    
    Throws:
    
    java.lang.Exception - if generation fails
  - countBagCiters
```
public void countBagCiters(Instance bag)
```
    calculates the citers associated to a bag
    
    Parameters:
    
    bag - the bag cited
  - countBagReferences
```
public void countBagReferences(Instance bag)
```
    Calculates the references of the exemplar bag
    
    Parameters:
    
    bag - the exemplar to which the nearest references will be calculated
  - distanceSet
```
public double distanceSet(Instance first,
                          Instance second)
```
    Calculates the distance between two instances
    
    Parameters:
    
    first - instance
    
    second - instance
    
    Returns:
    
    the distance value
  - distance
```
public double distance(Instance first,
                       Instance second)
```
    distance between two instances
    
    Parameters:
    
    first - the first instance
    
    second - the other instance
    
    Returns:
    
    the distance in double precision
  - distributionForInstance
```
public double[] distributionForInstance(Instance bag)
                                 throws java.lang.Exception
```
    Computes the distribution for a given exemplar
    
    Overrides:
    
    distributionForInstance in class Classifier
    
    Parameters:
    
    bag - the exemplar for which distribution is computed
    
    Returns:
    
    the distribution
    
    Throws:
    
    java.lang.Exception - if the distribution can't be computed successfully
  - updateNormalization
```
public void updateNormalization(Instance bag)
```
    Updates the normalization of each attribute.
    
    Parameters:
    
    bag - the exemplar to update the normalization for
  - equalExemplars
```
public boolean equalExemplars(Instance exemplar1,
                              Instance exemplar2)
```
    Wether the instances of two exemplars are or are not equal
    
    Parameters:
    
    exemplar1 - first exemplar
    
    exemplar2 - second exemplar
    
    Returns:
    
    if the instances of the exemplars are equal or not
  - listOptions
```
public java.util.Enumeration listOptions()
```
    Returns an enumeration of all the available options..
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class Classifier
    
    Returns:
    
    an enumeration of all available options.
  - setOptions
```
public void setOptions(java.lang.String[] options)
                throws java.lang.Exception
```
    Sets the OptionHandler's options using the given list. All options will be set (or reset) during this call (i.e. incremental setting of options is not possible).
    Valid options are:
    
    -R <number of references> Number of Nearest References (default 1)
    
    -C <number of citers> Number of Nearest Citers (default 1)
    
    -H <rank> Rank of the Hausdorff Distance (default 1)
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class Classifier
    
    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 option settings for the OptionHandler.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class Classifier
    
    Returns:
    
    the list of current option settings as an array of strings
  - toString
```
public java.lang.String toString()
```
    returns a string representation of the classifier
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    the string representation
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class Classifier
    
    Returns:
    
    the revision
  - main
```
public static void main(java.lang.String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    
    argv - should contain the command line arguments to the scheme (see Evaluation)

Class CitationKNN

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Constructor Detail

CitationKNN

Method Detail

globalInfo

getTechnicalInformation

preprocessData

HDRankTipText

setHDRank

getHDRank

numReferencesTipText

setNumReferences

getNumReferences

numCitersTipText

setNumCiters

getNumCiters

getCapabilities

getMultiInstanceCapabilities

buildClassifier

buildCNN

countBagCiters

countBagReferences

distanceSet

distance

distributionForInstance

updateNormalization

equalExemplars

listOptions

setOptions

getOptions

toString

getRevision

main