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## weka.classifiers.meta
Class AttributeSelectedClassifier

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

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

    public class AttributeSelectedClassifier

extends SingleClassifierEnhancer
implements OptionHandler, Drawable, AdditionalMeasureProducer, WeightedInstancesHandler

Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.

Valid options are:

     -E <attribute evaluator specification>
      Full class name of attribute evaluator, followed
      by its options.
      eg: "weka.attributeSelection.CfsSubsetEval -L"
      (default weka.attributeSelection.CfsSubsetEval)

     -S <search method specification>
      Full class name of search method, followed
      by its options.
      eg: "weka.attributeSelection.BestFirst -D 1"
      (default weka.attributeSelection.BestFirst)

     -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: 1.26 $
Author:
  Mark Hall (mhall@cs.waikato.ac.nz)
See Also:
  Serialized Form

Field Summary

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

Constructor Summary
`AttributeSelectedClassifier()` Default constructor.

Method Summary
`void` | `buildClassifier(Instances data)` Build the classifier on the dimensionally reduced data.
`double[]` | `distributionForInstance(Instance instance)` Classifies a given instance after attribute selection
`java.util.Enumeration` | `enumerateMeasures()` Returns an enumeration of the additional measure names
`java.lang.String` | `evaluatorTipText()` Returns the tip text for this property
`Capabilities` | `getCapabilities()` Returns default capabilities of the classifier.
`ASEvaluation` | `getEvaluator()` Gets the attribute evaluator used
`double` | `getMeasure(java.lang.String additionalMeasureName)` Returns the value of the named measure
`java.lang.String[]` | `getOptions()` Gets the current settings of the Classifier.
`java.lang.String` | `getRevision()` Returns the revision string.
`ASSearch` | `getSearch()` Gets the search method used
`java.lang.String` | `globalInfo()` Returns a string describing this search method
`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.
`double` | `measureNumAttributesSelected()` Additional measure --- number of attributes selected
`double` | `measureSelectionTime()` Additional measure --- time taken (milliseconds) to select the attributes
`double` | `measureTime()` Additional measure --- time taken (milliseconds) to select attributes and build the classifier
`java.lang.String` | `searchTipText()` Returns the tip text for this property
`void` | `setEvaluator(ASEvaluation evaluator)` Sets the attribute evaluator
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`void` | `setSearch(ASSearch search)` Sets the search method
`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

### AttributeSelectedClassifier

    public AttributeSelectedClassifier()

  Default constructor.

Method Detail

### globalInfo

    public java.lang.String globalInfo()

  Returns a string describing this search method

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

### 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:

       -E <attribute evaluator specification>
        Full class name of attribute evaluator, followed
        by its options.
        eg: "weka.attributeSelection.CfsSubsetEval -L"
        (default weka.attributeSelection.CfsSubsetEval)

       -S <search method specification>
        Full class name of search method, followed
        by its options.
        eg: "weka.attributeSelection.BestFirst -D 1"
        (default weka.attributeSelection.BestFirst)

       -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

### evaluatorTipText

    public java.lang.String evaluatorTipText()

  Returns the tip text for this property

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

### setEvaluator

    public void setEvaluator(ASEvaluation evaluator)

  Sets the attribute evaluator

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

### getEvaluator

    public ASEvaluation getEvaluator()

  Gets the attribute evaluator used

  Returns:
    the attribute evaluator

### searchTipText

    public java.lang.String searchTipText()

  Returns the tip text for this property

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

### setSearch

    public void setSearch(ASSearch search)

  Sets the search method

  Parameters:
    `search` - the search method with all options set.

### getSearch

    public ASSearch getSearch()

  Gets the search method used

  Returns:
    the search method

### 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 dimensionally reduced 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 attribute selection

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

### graphType

    public int graphType()

  Returns the type of graph this classifier represents.

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

### 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

### 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

### measureNumAttributesSelected

    public double measureNumAttributesSelected()

  Additional measure --- number of attributes selected

  Returns:
    the number of attributes selected

### measureSelectionTime

    public double measureSelectionTime()

  Additional measure --- time taken (milliseconds) to select the attributes

  Returns:
    the time taken to select attributes

### measureTime

    public double measureTime()

  Additional measure --- time taken (milliseconds) to select attributes and build the classifier

  Returns:
    the total time (select attributes + build classifier)

### enumerateMeasures

    public java.util.Enumeration enumerateMeasures()

  Returns an enumeration of the additional measure names

  Specified by:
    `enumerateMeasures` in interface `AdditionalMeasureProducer`
  Returns:
    an enumeration of the measure names

### getMeasure

    public double getMeasure(java.lang.String additionalMeasureName)

  Returns the value of the named measure

  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:
    `java.lang.IllegalArgumentException` - if the named measure is not supported

### 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 following arguments: -t training file [-T test file] [-c class index]

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