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java.lang.Objectweka.attributeSelection.ASSearch
weka.attributeSelection.RankSearch
public class RankSearch
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.
-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.
Constructor Summary | |
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RankSearch()
Constructor |
Method Summary | |
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java.lang.String |
attributeEvaluatorTipText()
Returns the tip text for this property |
ASEvaluation |
getAttributeEvaluator()
Get the attribute evaluator used to generate the ranking. |
java.lang.String[] |
getOptions()
Gets the current settings of WrapperSubsetEval. |
java.lang.String |
getRevision()
Returns the revision string. |
int |
getStartPoint()
Get the point at which to start evaluating the ranking |
int |
getStepSize()
Get the number of attributes to add from the rankining in each iteration |
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. |
java.lang.String |
globalInfo()
Returns a string describing this search method |
java.util.Enumeration |
listOptions()
Returns an enumeration describing the available options. |
int[] |
search(ASEvaluation ASEval,
Instances data)
Ranks attributes using the specified attribute evaluator and then searches the ranking using the supplied subset evaluator. |
void |
setAttributeEvaluator(ASEvaluation newEvaluator)
Set the attribute evaluator to use for generating the ranking. |
void |
setOptions(java.lang.String[] options)
Parses a given list of options. |
void |
setStartPoint(int sp)
Set the point at which to start evaluating the ranking |
void |
setStepSize(int ss)
Set the number of attributes to add from the rankining in each iteration |
java.lang.String |
startPointTipText()
Returns the tip text for this property |
java.lang.String |
stepSizeTipText()
Returns the tip text for this property |
java.lang.String |
toString()
returns a description of the search as a String |
Methods inherited from class weka.attributeSelection.ASSearch |
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forName, makeCopies |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Constructor Detail |
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public RankSearch()
Method Detail |
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public java.lang.String globalInfo()
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation
in interface TechnicalInformationHandler
public java.lang.String attributeEvaluatorTipText()
public void setAttributeEvaluator(ASEvaluation newEvaluator)
newEvaluator
- the attribute evaluator to use.public ASEvaluation getAttributeEvaluator()
public java.lang.String stepSizeTipText()
public void setStepSize(int ss)
ss
- the number of attribes to add.public int getStepSize()
public java.lang.String startPointTipText()
public void setStartPoint(int sp)
sp
- the position in the ranking to start atpublic int getStartPoint()
public java.util.Enumeration listOptions()
listOptions
in interface OptionHandler
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-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.
setOptions
in interface OptionHandler
options
- the list of options as an array of strings
java.lang.Exception
- if an option is not supportedpublic java.lang.String[] getOptions()
getOptions
in interface OptionHandler
public int[] search(ASEvaluation ASEval, Instances data) throws java.lang.Exception
search
in class ASSearch
ASEval
- the subset evaluator to guide the searchdata
- the training instances.
java.lang.Exception
- if the search can't be completedpublic java.lang.String toString()
toString
in class java.lang.Object
public java.lang.String getRevision()
getRevision
in interface RevisionHandler
getRevision
in class ASSearch
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