Class RankSearch

java.lang.Object
weka.attributeSelection.ASSearch
weka.attributeSelection.RankSearch
All Implemented Interfaces:
Serializable, OptionHandler, RevisionHandler, TechnicalInformationHandler

public class RankSearch extends ASSearch implements OptionHandler, TechnicalInformationHandler
RankSearch :

Uses an attribute/subset evaluator to rank all attributes. If a subset evaluator is specified, then a forward selection search is used to generate a ranked list. From the ranked list of attributes, subsets of increasing size are evaluated, ie. The best attribute, the best attribute plus the next best attribute, etc.... The best attribute set is reported. RankSearch is linear in the number of attributes if a simple attribute evaluator is used such as GainRatioAttributeEval. For more information see:

Mark Hall, Geoffrey Holmes (2003). Benchmarking attribute selection techniques for discrete class data mining. IEEE Transactions on Knowledge and Data Engineering. 15(6):1437-1447.

Valid options are:

 -A <attribute evaluator>
  class name of attribute evaluator to use for ranking. Place any
  evaluator options LAST on the command line following a "--".
  eg.:
   -A weka.attributeSelection.GainRatioAttributeEval ... -- -M
  (default: weka.attributeSelection.GainRatioAttributeEval)
 -S <step size>
  number of attributes to be added from the
  ranking in each iteration (default = 1).
 -R <start point>
  point in the ranking to start evaluating from. 
  (default = 0, ie. the head of the ranking).
 
 Options specific to evaluator weka.attributeSelection.GainRatioAttributeEval:
 
 -M
  treat missing values as a seperate value.
Version:
$Revision: 6253 $
Author:
Mark Hall (mhall@cs.waikato.ac.nz)
See Also:
  • Constructor Details

    • RankSearch

      public RankSearch()
      Constructor
  • Method Details

    • globalInfo

      public 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.
      Specified by:
      getTechnicalInformation in interface TechnicalInformationHandler
      Returns:
      the technical information about this class
    • attributeEvaluatorTipText

      public String attributeEvaluatorTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui
    • setAttributeEvaluator

      public void setAttributeEvaluator(ASEvaluation newEvaluator)
      Set the attribute evaluator to use for generating the ranking.
      Parameters:
      newEvaluator - the attribute evaluator to use.
    • getAttributeEvaluator

      public ASEvaluation getAttributeEvaluator()
      Get the attribute evaluator used to generate the ranking.
      Returns:
      the evaluator used to generate the ranking.
    • stepSizeTipText

      public String stepSizeTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui
    • setStepSize

      public void setStepSize(int ss)
      Set the number of attributes to add from the rankining in each iteration
      Parameters:
      ss - the number of attribes to add.
    • getStepSize

      public int getStepSize()
      Get the number of attributes to add from the rankining in each iteration
      Returns:
      the number of attributes to add.
    • startPointTipText

      public String startPointTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui
    • setStartPoint

      public void setStartPoint(int sp)
      Set the point at which to start evaluating the ranking
      Parameters:
      sp - the position in the ranking to start at
    • getStartPoint

      public int getStartPoint()
      Get the point at which to start evaluating the ranking
      Returns:
      the position in the ranking to start at
    • listOptions

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

      Valid options are:

       -A <attribute evaluator>
        class name of attribute evaluator to use for ranking. Place any
        evaluator options LAST on the command line following a "--".
        eg.:
         -A weka.attributeSelection.GainRatioAttributeEval ... -- -M
        (default: weka.attributeSelection.GainRatioAttributeEval)
       -S <step size>
        number of attributes to be added from the
        ranking in each iteration (default = 1).
       -R <start point>
        point in the ranking to start evaluating from. 
        (default = 0, ie. the head of the ranking).
       
       Options specific to evaluator weka.attributeSelection.GainRatioAttributeEval:
       
       -M
        treat missing values as a seperate value.
      Specified by:
      setOptions in interface OptionHandler
      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 WrapperSubsetEval.
      Specified by:
      getOptions in interface OptionHandler
      Returns:
      an array of strings suitable for passing to setOptions()
    • search

      public int[] search(ASEvaluation ASEval, Instances data) throws Exception
      Ranks attributes using the specified attribute evaluator and then searches the ranking using the supplied subset evaluator.
      Specified by:
      search in class ASSearch
      Parameters:
      ASEval - the subset evaluator to guide the search
      data - the training instances.
      Returns:
      an array (not necessarily ordered) of selected attribute indexes
      Throws:
      Exception - if the search can't be completed
    • toString

      public String toString()
      returns a description of the search as a String
      Overrides:
      toString in class Object
      Returns:
      a description of the search
    • getRevision

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