Package weka.classifiers.lazy

Class IBk

java.lang.Object
weka.classifiers.AbstractClassifier
weka.classifiers.lazy.IBk
All Implemented Interfaces:
Serializable, Cloneable, Classifier, UpdateableClassifier, AdditionalMeasureProducer, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler
public class IBk extends AbstractClassifier implements OptionHandler, UpdateableClassifier, WeightedInstancesHandler, TechnicalInformationHandler, AdditionalMeasureProducer
K-nearest neighbours classifier. Can select appropriate value of K based on cross-validation. Can also do distance weighting.

For more information, see

D. Aha, D. Kibler (1991). Instance-based learning algorithms. Machine Learning. 6:37-66.

BibTeX: 

 @article{Aha1991,
    author = {D. Aha and D. Kibler},
    journal = {Machine Learning},
    pages = {37-66},
    title = {Instance-based learning algorithms},
    volume = {6},
    year = {1991}
 }

Valid options are: 

 -I
  Weight neighbours by the inverse of their distance
  (use when k > 1)
 -F
  Weight neighbours by 1 - their distance
  (use when k > 1)
 -K <number of neighbors>
  Number of nearest neighbours (k) used in classification.
  (Default = 1)
 -E
  Minimise mean squared error rather than mean absolute
  error when using -X option with numeric prediction.
 -W <window size>
  Maximum number of training instances maintained.
  Training instances are dropped FIFO. (Default = no window)
 -X
  Select the number of nearest neighbours between 1
  and the k value specified using hold-one-out evaluation
  on the training data (use when k > 1)
 -A
  The nearest neighbour search algorithm to use (default: weka.core.neighboursearch.LinearNNSearch).
Version:
$Revision: 15519 $
Author:
Stuart Inglis (singlis@cs.waikato.ac.nz), Len Trigg (trigg@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)
See Also:

  • Field Details

    • WEIGHT_NONE

      public static final int WEIGHT_NONE
      no weighting.
      See Also:

    • WEIGHT_INVERSE

      public static final int WEIGHT_INVERSE
      weight by 1/distance.
      See Also:

    • WEIGHT_SIMILARITY

      public static final int WEIGHT_SIMILARITY
      weight by 1-distance.
      See Also:

    • TAGS_WEIGHTING

      public static final Tag[] TAGS_WEIGHTING
      possible instance weighting methods.

  • Constructor Details

    • IBk

      public IBk(int k)
      IBk classifier. Simple instance-based learner that uses the class of the nearest k training instances for the class of the test instances.
      Parameters:
      k - the number of nearest neighbors to use for prediction

    • IBk

      public IBk()
      IB1 classifer. Instance-based learner. Predicts the class of the single nearest training instance for each test instance.

  • Method Details

    • globalInfo

      public String globalInfo()
      Returns a string describing classifier.
      Returns:
      a description 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

    • KNNTipText

      public String KNNTipText()
      Returns the tip text for this property.
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • setKNN

      public void setKNN(int k)
      Set the number of neighbours the learner is to use.
      Parameters:
      k - the number of neighbours.

    • getKNN

      public int getKNN()
      Gets the number of neighbours the learner will use.
      Returns:
      the number of neighbours.

    • windowSizeTipText

      public String windowSizeTipText()
      Returns the tip text for this property.
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • getWindowSize

      public int getWindowSize()
      Gets the maximum number of instances allowed in the training pool. The addition of new instances above this value will result in old instances being removed. A value of 0 signifies no limit to the number of training instances.
      Returns:
      Value of WindowSize.

    • setWindowSize

      public void setWindowSize(int newWindowSize)
      Sets the maximum number of instances allowed in the training pool. The addition of new instances above this value will result in old instances being removed. A value of 0 signifies no limit to the number of training instances.
      Parameters:
      newWindowSize - Value to assign to WindowSize.

    • distanceWeightingTipText

      public String distanceWeightingTipText()
      Returns the tip text for this property.
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • getDistanceWeighting

      public SelectedTag getDistanceWeighting()
      Gets the distance weighting method used. Will be one of WEIGHT_NONE, WEIGHT_INVERSE, or WEIGHT_SIMILARITY
      Returns:
      the distance weighting method used.

    • setDistanceWeighting

      public void setDistanceWeighting(SelectedTag newMethod)
      Sets the distance weighting method used. Values other than WEIGHT_NONE, WEIGHT_INVERSE, or WEIGHT_SIMILARITY will be ignored.
      Parameters:
      newMethod - the distance weighting method to use

    • meanSquaredTipText

      public String meanSquaredTipText()
      Returns the tip text for this property.
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • getMeanSquared

      public boolean getMeanSquared()
      Gets whether the mean squared error is used rather than mean absolute error when doing cross-validation.
      Returns:
      true if so.

    • setMeanSquared

      public void setMeanSquared(boolean newMeanSquared)
      Sets whether the mean squared error is used rather than mean absolute error when doing cross-validation.
      Parameters:
      newMeanSquared - true if so.

    • crossValidateTipText

      public String crossValidateTipText()
      Returns the tip text for this property.
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • getCrossValidate

      public boolean getCrossValidate()
      Gets whether hold-one-out cross-validation will be used to select the best k value.
      Returns:
      true if cross-validation will be used.

    • setCrossValidate

      public void setCrossValidate(boolean newCrossValidate)
      Sets whether hold-one-out cross-validation will be used to select the best k value.
      Parameters:
      newCrossValidate - true if cross-validation should be used.

    • nearestNeighbourSearchAlgorithmTipText

      public String nearestNeighbourSearchAlgorithmTipText()
      Returns the tip text for this property.
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • getNearestNeighbourSearchAlgorithm

      public NearestNeighbourSearch getNearestNeighbourSearchAlgorithm()
      Returns the current nearestNeighbourSearch algorithm in use.
      Returns:
      the NearestNeighbourSearch algorithm currently in use.

    • setNearestNeighbourSearchAlgorithm

      public void setNearestNeighbourSearchAlgorithm(NearestNeighbourSearch nearestNeighbourSearchAlgorithm)
      Sets the nearestNeighbourSearch algorithm to be used for finding nearest neighbour(s).
      Parameters:
      nearestNeighbourSearchAlgorithm - - The NearestNeighbourSearch class.

    • getNumTraining

      public int getNumTraining()
      Get the number of training instances the classifier is currently using.
      Returns:
      the number of training instances the classifier is currently using

    • getCapabilities

      public Capabilities getCapabilities()
      Returns default capabilities of the classifier.
      Specified by:
      getCapabilities in interface CapabilitiesHandler
      Specified by:
      getCapabilities in interface Classifier
      Overrides:
      getCapabilities in class AbstractClassifier
      Returns:
      the capabilities of this classifier
      See Also:

    • buildClassifier

      public void buildClassifier(Instances instances) throws Exception
      Generates the classifier.
      Specified by:
      buildClassifier in interface Classifier
      Parameters:
      instances - set of instances serving as training data
      Throws:
      Exception - if the classifier has not been generated successfully

    • updateClassifier

      public void updateClassifier(Instance instance) throws Exception
      Adds the supplied instance to the training set.
      Specified by:
      updateClassifier in interface UpdateableClassifier
      Parameters:
      instance - the instance to add
      Throws:
      Exception - if instance could not be incorporated successfully

    • distributionForInstance

      public double[] distributionForInstance(Instance instance) throws Exception
      Calculates the class membership probabilities for the given test instance.
      Specified by:
      distributionForInstance in interface Classifier
      Overrides:
      distributionForInstance in class AbstractClassifier
      Parameters:
      instance - the instance to be classified
      Returns:
      predicted class probability distribution
      Throws:
      Exception - if an error occurred during the prediction

    • listOptions

      public Enumeration<Option> listOptions()
      Returns an enumeration describing the available options.
      Specified by:
      listOptions in interface OptionHandler
      Overrides:
      listOptions in class AbstractClassifier
      Returns:
      an enumeration of all the available options.

    • setOptions

      public void setOptions(String[] options) throws Exception
      Parses a given list of options.

      Valid options are: 

       -I
  Weight neighbours by the inverse of their distance
  (use when k > 1)
       -F
  Weight neighbours by 1 - their distance
  (use when k > 1)
       -K <number of neighbors>
  Number of nearest neighbours (k) used in classification.
  (Default = 1)
       -E
  Minimise mean squared error rather than mean absolute
  error when using -X option with numeric prediction.
       -W <window size>
  Maximum number of training instances maintained.
  Training instances are dropped FIFO. (Default = no window)
       -X
  Select the number of nearest neighbours between 1
  and the k value specified using hold-one-out evaluation
  on the training data (use when k > 1)
       -A
  The nearest neighbour search algorithm to use (default: weka.core.neighboursearch.LinearNNSearch).
      Specified by:
      setOptions in interface OptionHandler
      Overrides:
      setOptions in class AbstractClassifier
      Parameters:
      options - the list of options as an array of strings
      Throws:
      Exception - if an option is not supported

    • getOptions

      public String[] getOptions()
      Gets the current settings of IBk.
      Specified by:
      getOptions in interface OptionHandler
      Overrides:
      getOptions in class AbstractClassifier
      Returns:
      an array of strings suitable for passing to setOptions()

    • enumerateMeasures

      public Enumeration<String> enumerateMeasures()
      Returns an enumeration of the additional measure names produced by the neighbour search algorithm, plus the chosen K in case cross-validation is enabled.
      Specified by:
      enumerateMeasures in interface AdditionalMeasureProducer
      Returns:
      an enumeration of the measure names

    • getMeasure

      public double getMeasure(String additionalMeasureName)
      Returns the value of the named measure from the neighbour search algorithm, plus the chosen K in case cross-validation is enabled.
      Specified by:
      getMeasure in interface AdditionalMeasureProducer
      Parameters:
      additionalMeasureName - the name of the measure to query for its value
      Returns:
      the value of the named measure
      Throws:
      IllegalArgumentException - if the named measure is not supported

    • toString

      public String toString()
      Returns a description of this classifier.
      Overrides:
      toString in class Object
      Returns:
      a description of this classifier as a string.

    • pruneToK

      public Instances pruneToK(Instances neighbours, double[] distances, int k)
      Prunes the list to contain the k nearest neighbors. If there are multiple neighbors at the k'th distance, all will be kept.
      Parameters:
      neighbours - the neighbour instances.
      distances - the distances of the neighbours from target instance.
      k - the number of neighbors to keep.
      Returns:
      the pruned neighbours.

    • getRevision

      public String getRevision()
      Returns the revision string.
      Specified by:
      getRevision in interface RevisionHandler
      Overrides:
      getRevision in class AbstractClassifier
      Returns:
      the revision

    • main

      public static void main(String[] argv)
      Main method for testing this class.
      Parameters:
      argv - should contain command line options (see setOptions)