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## weka.attributeSelection
Class SubsetSizeForwardSelection

    java.lang.Object
      weka.attributeSelection.ASSearch
          weka.attributeSelection.SubsetSizeForwardSelection

All Implemented Interfaces:
  java.io.Serializable, OptionHandler, RevisionHandler

    public class SubsetSizeForwardSelection

extends ASSearch
implements OptionHandler

SubsetSizeForwardSelection:
Extension of LinearForwardSelection. The search performs an interior cross-validation (seed and number of folds can be specified). A LinearForwardSelection is performed on each foldto determine the optimal subset-size (using the given SubsetSizeEvaluator). Finally, a LinearForwardSelection up to the optimal subset-size is performed on the whole data.
For more information see:
Martin Guetlein (2006). Large Scale Attribute Selection Using Wrappers. Freiburg, Germany.

Valid options are:

     -I
      Perform initial ranking to select the
      top-ranked attributes.

     -K <num>
      Number of top-ranked attributes that are
      taken into account by the search.

     -T <0 = fixed-set | 1 = fixed-width>
      Type of Linear Forward Selection (default = 0).

     -S <num>
      Size of lookup cache for evaluated subsets.
      Expressed as a multiple of the number of
      attributes in the data set. (default = 1)

     -E <subset evaluator>
      Subset-evaluator used for subset-size determination.-- -M

     -F <num>
      Number of cross validation folds
      for subset size determination (default = 5).

     -R <num>
      Seed for cross validation
      subset size determination. (default = 1)

     -Z
      verbose on/off

     Options specific to evaluator weka.attributeSelection.ClassifierSubsetEval:

     -B <classifier>
      class name of the classifier to use for accuracy estimation.
      Place any classifier options LAST on the command line
      following a "--". eg.:
       -B weka.classifiers.bayes.NaiveBayes ... -- -K
      (default: weka.classifiers.rules.ZeroR)

     -T
      Use the training data to estimate accuracy.

     -H <filename>
      Name of the hold out/test set to
      estimate accuracy on.

     Options specific to scheme weka.classifiers.rules.ZeroR:

     -D
      If set, classifier is run in debug mode and
      may output additional info to the console

Version:
  $Revision: 5605 $
Author:
  Martin Guetlein (martin.guetlein@gmail.com)
See Also:
  Serialized Form

Field Summary
`static Tag[]` | `TAGS_TYPE`

Constructor Summary
`SubsetSizeForwardSelection()` Constructor

Method Summary
`int` | `getLookupCacheSize()` Return the maximum size of the evaluated subset cache (expressed as a multiplier for the number of attributes in a data set.
`int` | `getNumSubsetSizeCVFolds()` Get the number of cross validation folds for subset size determination (default = 5).
`int` | `getNumUsedAttributes()` Get the number of top-ranked attributes that taken into account by the search process.
`java.lang.String[]` | `getOptions()` Gets the current settings of LinearForwardSelection.
`boolean` | `getPerformRanking()` Get boolean if initial ranking should be performed to select the top-ranked attributes
`java.lang.String` | `getRevision()` Returns the revision string.
`int` | `getSeed()` Seed for cross validation subset size determination.
`ASEvaluation` | `getSubsetSizeEvaluator()` Get the subset evaluator used for subset size determination.
`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` | `getType()` Get the type
`boolean` | `getVerbose()` Get whether output is to be verbose
`java.lang.String` | `globalInfo()` Returns a string describing this search method
`java.util.Enumeration` | `listOptions()` Returns an enumeration describing the available options.
`java.lang.String` | `lookupCacheSizeTipText()` Returns the tip text for this property
`java.lang.String` | `numSubsetSizeCVFoldsTipText()` Returns the tip text for this property
`java.lang.String` | `numUsedAttributesTipText()` Returns the tip text for this property
`java.lang.String` | `performRankingTipText()` Returns the tip text for this property
`int[]` | `search(ASEvaluation ASEval, Instances data)` Searches the attribute subset space by subset size forward selection
`java.lang.String` | `seedTipText()` Returns the tip text for this property
`void` | `setLookupCacheSize(int size)` Set the maximum size of the evaluated subset cache (hashtable).
`void` | `setNumSubsetSizeCVFolds(int f)` Set the number of cross validation folds for subset size determination (default = 5).
`void` | `setNumUsedAttributes(int k)` Set the number of top-ranked attributes that taken into account by the search process.
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`void` | `setPerformRanking(boolean b)` Perform initial ranking to select top-ranked attributes.
`void` | `setSeed(int s)` Seed for cross validation subset size determination.
`void` | `setSubsetSizeEvaluator(ASEvaluation eval)` Set the subset evaluator to use for subset size determination.
`void` | `setType(SelectedTag t)` Set the type
`void` | `setVerbose(boolean b)` Set whether verbose output should be generated.
`java.lang.String` | `subsetSizeEvaluatorTipText()` Returns the tip text for this property
`java.lang.String` | `toString()` returns a description of the search as a String
`java.lang.String` | `typeTipText()` Returns the tip text for this property
`java.lang.String` | `verboseTipText()` Returns the tip text for this property

Methods inherited from class weka.attributeSelection.ASSearch
`forName, makeCopies`

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

Field Detail

### TAGS_TYPE

    public static final Tag[] TAGS_TYPE

Constructor Detail

### SubsetSizeForwardSelection

    public SubsetSizeForwardSelection()

  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

### 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.

  Returns:
    the technical information about this class

### listOptions

    public java.util.Enumeration listOptions()

  Returns an enumeration describing the available options.

  Specified by:
    `listOptions` in interface `OptionHandler`
  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:

  -I
  Perform initial ranking to select top-ranked attributes.

  -K
  Number of top-ranked attributes that are taken into account.

  -T <0 = fixed-set | 1 = fixed-width>
  Typ of Linear Forward Selection (default = 0).

  -S
  Size of lookup cache for evaluated subsets. Expressed as a multiple of the number of attributes in the data set. (default = 1).

  -E
  class name of subset evaluator to use for subset size determination (default = null, same subset evaluator as for ranking and final forward selection is used). Place any evaluator options LAST on the command line following a "--". eg. -A weka.attributeSelection.ClassifierSubsetEval ... -- -M -F
  Number of cross validation folds for subset size determination (default = 5).

  -R
  Seed for cross validation subset size determination. (default = 1)

  -Z
  verbose on/off.

  Specified by:
    `setOptions` in interface `OptionHandler`
  Parameters:
    `options` - the list of options as an array of strings
  Throws:
    `java.lang.Exception` - if an option is not supported

### setLookupCacheSize

    public void setLookupCacheSize(int size)

  Set the maximum size of the evaluated subset cache (hashtable). This is expressed as a multiplier for the number of attributes in the data set. (default = 1).

  Parameters:
    `size` - the maximum size of the hashtable

### getLookupCacheSize

    public int getLookupCacheSize()

  Return the maximum size of the evaluated subset cache (expressed as a multiplier for the number of attributes in a data set.

  Returns:
    the maximum size of the hashtable.

### lookupCacheSizeTipText

    public java.lang.String lookupCacheSizeTipText()

  Returns the tip text for this property

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

### performRankingTipText

    public java.lang.String performRankingTipText()

  Returns the tip text for this property

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

### setPerformRanking

    public void setPerformRanking(boolean b)

  Perform initial ranking to select top-ranked attributes.

  Parameters:
    `b` - true if initial ranking should be performed

### getPerformRanking

    public boolean getPerformRanking()

  Get boolean if initial ranking should be performed to select the top-ranked attributes

  Returns:
    true if initial ranking should be performed

### numUsedAttributesTipText

    public java.lang.String numUsedAttributesTipText()

  Returns the tip text for this property

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

### setNumUsedAttributes

    public void setNumUsedAttributes(int k)
                              throws java.lang.Exception

  Set the number of top-ranked attributes that taken into account by the search process.

  Parameters:
    `k` - the number of attributes
  Throws:
    `java.lang.Exception` - if k is less than 2

### getNumUsedAttributes

    public int getNumUsedAttributes()

  Get the number of top-ranked attributes that taken into account by the search process.

  Returns:
    the number of top-ranked attributes that taken into account

### typeTipText

    public java.lang.String typeTipText()

  Returns the tip text for this property

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

### setType

    public void setType(SelectedTag t)

  Set the type

  Parameters:
    `t` - the Linear Forward Selection type

### getType

    public SelectedTag getType()

  Get the type

  Returns:
    the Linear Forward Selection type

### subsetSizeEvaluatorTipText

    public java.lang.String subsetSizeEvaluatorTipText()

  Returns the tip text for this property

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

### setSubsetSizeEvaluator

    public void setSubsetSizeEvaluator(ASEvaluation eval)
                                throws java.lang.Exception

  Set the subset evaluator to use for subset size determination.

  Parameters:
    `eval` - the subset evaluator to use for subset size determination.
  Throws:
    `java.lang.Exception`

### getSubsetSizeEvaluator

    public ASEvaluation getSubsetSizeEvaluator()

  Get the subset evaluator used for subset size determination.

  Returns:
    the evaluator used for subset size determination.

### numSubsetSizeCVFoldsTipText

    public java.lang.String numSubsetSizeCVFoldsTipText()

  Returns the tip text for this property

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

### setNumSubsetSizeCVFolds

    public void setNumSubsetSizeCVFolds(int f)

  Set the number of cross validation folds for subset size determination (default = 5).

  Parameters:
    `f` - number of folds

### getNumSubsetSizeCVFolds

    public int getNumSubsetSizeCVFolds()

  Get the number of cross validation folds for subset size determination (default = 5).

  Returns:
    number of folds

### seedTipText

    public java.lang.String seedTipText()

  Returns the tip text for this property

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

### setSeed

    public void setSeed(int s)

  Seed for cross validation subset size determination. (default = 1)

  Parameters:
    `s` - seed

### getSeed

    public int getSeed()

  Seed for cross validation subset size determination. (default = 1)

  Returns:
    seed

### verboseTipText

    public java.lang.String verboseTipText()

  Returns the tip text for this property

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

### setVerbose

    public void setVerbose(boolean b)

  Set whether verbose output should be generated.

  Parameters:
    `d` - true if output is to be verbose.

### getVerbose

    public boolean getVerbose()

  Get whether output is to be verbose

  Returns:
    true if output will be verbose

### getOptions

    public java.lang.String[] getOptions()

  Gets the current settings of LinearForwardSelection.

  Specified by:
    `getOptions` in interface `OptionHandler`
  Returns:
    an array of strings suitable for passing to setOptions()

### toString

    public java.lang.String toString()

  returns a description of the search as a String

  Overrides:
    `toString` in class `java.lang.Object`
  Returns:
    a description of the search

### search

    public int[] search(ASEvaluation ASEval,
                        Instances data)
                 throws java.lang.Exception

  Searches the attribute subset space by subset size forward selection

  Specified by:
    `search` in class `ASSearch`
  Parameters:
    `ASEvaluator` - the attribute evaluator to guide the search
    `data` - the training instances.
  Returns:
    an array (not necessarily ordered) of selected attribute indexes
  Throws:
    `java.lang.Exception` - if the search can't be completed

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

  Specified by:
    `getRevision` in interface `RevisionHandler`
  Overrides:
    `getRevision` in class `ASSearch`
  Returns:
    the revision

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