FilteredClassifier

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## weka.classifiers.meta
Class FilteredClassifier

    java.lang.Object
      weka.classifiers.Classifier
          weka.classifiers.SingleClassifierEnhancer
              weka.classifiers.meta.FilteredClassifier

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

    public class FilteredClassifier

extends SingleClassifierEnhancer
implements Drawable

Class for running an arbitrary classifier on data that has been passed through an arbitrary filter. Like the classifier, the structure of the filter is based exclusively on the training data and test instances will be processed by the filter without changing their structure.

Valid options are:

     -F <filter specification>
      Full class name of filter to use, followed
      by filter options.
      eg: "weka.filters.unsupervised.attribute.Remove -V -R 1,2"

     -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.trees.J48)

     Options specific to classifier weka.classifiers.trees.J48:

     -U
      Use unpruned tree.

     -C <pruning confidence>
      Set confidence threshold for pruning.
      (default 0.25)

     -M <minimum number of instances>
      Set minimum number of instances per leaf.
      (default 2)

     -R
      Use reduced error pruning.

     -N <number of folds>
      Set number of folds for reduced error
      pruning. One fold is used as pruning set.
      (default 3)

     -B
      Use binary splits only.

     -S
      Don't perform subtree raising.

     -L
      Do not clean up after the tree has been built.

     -A
      Laplace smoothing for predicted probabilities.

     -Q <seed>
      Seed for random data shuffling (default 1).

Version:
  $Revision: 1.28 $
Author:
  Len Trigg (trigg@cs.waikato.ac.nz)
See Also:
  Serialized Form

Field Summary

Fields inherited from interface weka.core.Drawable
`BayesNet, NOT_DRAWABLE, TREE`

Constructor Summary
`FilteredClassifier()` Default constructor.

Method Summary
`void` | `buildClassifier(Instances data)` Build the classifier on the filtered data.
`double[]` | `distributionForInstance(Instance instance)` Classifies a given instance after filtering.
`java.lang.String` | `filterTipText()` Returns the tip text for this property
`Capabilities` | `getCapabilities()` Returns default capabilities of the classifier.
`Filter` | `getFilter()` Gets the filter used.
`java.lang.String[]` | `getOptions()` Gets the current settings of the Classifier.
`java.lang.String` | `getRevision()` Returns the revision string.
`java.lang.String` | `globalInfo()` Returns a string describing this classifier
`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.
`void` | `setFilter(Filter filter)` Sets the filter
`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.SingleClassifierEnhancer
`classifierTipText, getClassifier, setClassifier`

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

### FilteredClassifier

    public FilteredClassifier()

  Default constructor.

Method Detail

### globalInfo

    public java.lang.String globalInfo()

  Returns a string describing this classifier

  Returns:
    a description of the classifier suitable for displaying in the explorer/experimenter gui

### graphType

    public int graphType()

  Returns the type of graph this classifier represents.

  Specified by:
    `graphType` in interface `Drawable`
  Returns:
    the graph type of this classifier

### graph

    public java.lang.String graph()
                           throws java.lang.Exception

  Returns graph describing the classifier (if possible).

  Specified by:
    `graph` in interface `Drawable`
  Returns:
    the graph of the classifier in dotty format
  Throws:
    `java.lang.Exception` - if the classifier cannot be graphed

### listOptions

    public java.util.Enumeration listOptions()

  Returns an enumeration describing the available options.

  Specified by:
    `listOptions` in interface `OptionHandler`
  Overrides:
    `listOptions` in class `SingleClassifierEnhancer`
  Returns:
    an enumeration of all the available options.

### setOptions

    public void setOptions(java.lang.String[] options)
                    throws java.lang.Exception

  Parses a given list of options.

  Valid options are:

       -F <filter specification>
        Full class name of filter to use, followed
        by filter options.
        eg: "weka.filters.unsupervised.attribute.Remove -V -R 1,2"

       -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.trees.J48)

       Options specific to classifier weka.classifiers.trees.J48:

       -U
        Use unpruned tree.

       -C <pruning confidence>
        Set confidence threshold for pruning.
        (default 0.25)

       -M <minimum number of instances>
        Set minimum number of instances per leaf.
        (default 2)

       -R
        Use reduced error pruning.

       -N <number of folds>
        Set number of folds for reduced error
        pruning. One fold is used as pruning set.
        (default 3)

       -B
        Use binary splits only.

       -S
        Don't perform subtree raising.

       -L
        Do not clean up after the tree has been built.

       -A
        Laplace smoothing for predicted probabilities.

       -Q <seed>
        Seed for random data shuffling (default 1).

  Specified by:
    `setOptions` in interface `OptionHandler`
  Overrides:
    `setOptions` in class `SingleClassifierEnhancer`
  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 the Classifier.

  Specified by:
    `getOptions` in interface `OptionHandler`
  Overrides:
    `getOptions` in class `SingleClassifierEnhancer`
  Returns:
    an array of strings suitable for passing to setOptions

### filterTipText

    public java.lang.String filterTipText()

  Returns the tip text for this property

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### setFilter

    public void setFilter(Filter filter)

  Sets the filter

  Parameters:
    `filter` - the filter with all options set.

### getFilter

    public Filter getFilter()

  Gets the filter used.

  Returns:
    the filter

### getCapabilities

    public Capabilities getCapabilities()

  Returns default capabilities of the classifier.

  Specified by:
    `getCapabilities` in interface `CapabilitiesHandler`
  Overrides:
    `getCapabilities` in class `SingleClassifierEnhancer`
  Returns:
    the capabilities of this classifier
  See Also:
    `Capabilities`

### buildClassifier

    public void buildClassifier(Instances data)
                         throws java.lang.Exception

  Build the classifier on the filtered data.

  Specified by:
    `buildClassifier` in class `Classifier`
  Parameters:
    `data` - the training data
  Throws:
    `java.lang.Exception` - if the classifier could not be built successfully

### distributionForInstance

    public double[] distributionForInstance(Instance instance)
                                     throws java.lang.Exception

  Classifies a given instance after filtering.

  Overrides:
    `distributionForInstance` in class `Classifier`
  Parameters:
    `instance` - the instance to be classified
  Returns:
    the class distribution for the given instance
  Throws:
    `java.lang.Exception` - if instance could not be classified successfully

### toString

    public java.lang.String toString()

  Output a representation of this classifier

  Overrides:
    `toString` in class `java.lang.Object`
  Returns:
    a representation of this classifier

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

  Specified by:
    `getRevision` in interface `RevisionHandler`
  Returns:
    the revision

### main

    public static void main(java.lang.String[] argv)

  Main method for testing this class.

  Parameters:
    `argv` - should contain the following arguments: -t training file [-T test file] [-c class index]

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