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java.lang.Object | +----weka.attributeSelection.ASSearch | +----weka.attributeSelection.RaceSearch
For more information see:
Moore, A. W. and Lee, M. S. (1994). Efficient algorithms for minimising
cross validation error. Proceedings of the Eleventh International
Conference on Machine Learning. pp 190--198.
Valid options are:
-R
-L
-T
-F
-A
-Q
-N
-J
-Z
-R
-L
-T
-F
-A
-Q
-N
-J
-Z
0 = forward, 1 = backward, 2 = schemata, 3 = rank.
significance level to use for t-tests.
threshold for considering mean errors of two subsets the same
0 = 10 fold, 1 = leave-one-out (selected automatically for schemata race
the attribute evaluator to use when doing a rank search
produce a ranked list of attributes. Selecting this option forces
the race type to be forward. Racing continues until *all* attributes
have been selected, thus producing a ranked list of attributes.
Specify the number of attributes to retain. Overides any threshold.
Use in conjunction with -Q.
Specify a threshold by which the AttributeSelection module can discard
attributes. Use in conjunction with -Q.
Turn on verbose output for monitoring the search
attributeEvaluatorTipText()
debugTipText()
foldsTipText()
generateRankingTipText()
getAttributeEvaluator()
getCalculatedNumToSelect()
getDebug()
getFoldsType()
getGenerateRanking()
getNumToSelect()
getOptions()
getRaceType()
getSelectionThreshold()
getSignificanceLevel()
getThreshold()
globalInfo()
listOptions()
numToSelectTipText()
raceTypeTipText()
rankedAttributes()
search(ASEvaluation, Instances)
selectionThresholdTipText()
setAttributeEvaluator(ASEvaluation)
setDebug(boolean)
setFoldsType(SelectedTag)
setGenerateRanking(boolean)
setNumToSelect(int)
setOptions(String[])
setRaceType(SelectedTag)
setSelectionThreshold(double)
setSignificanceLevel(double)
setThreshold(double)
significanceLevelTipText()
thresholdTipText()
toString()
TAGS_SELECTION
public static final Tag[] TAGS_SELECTION
XVALTAGS_SELECTION
public static final Tag[] XVALTAGS_SELECTION
RaceSearch
public RaceSearch()
globalInfo
public java.lang.String globalInfo()
Returns a string describing this search method
raceTypeTipText
public java.lang.String raceTypeTipText()
Returns the tip text for this property
setRaceType
public void setRaceType(SelectedTag d)
Set the race type
d
- the type of race
getRaceType
public SelectedTag getRaceType()
Get the race type
significanceLevelTipText
public java.lang.String significanceLevelTipText()
Returns the tip text for this property
setSignificanceLevel
public void setSignificanceLevel(double sig)
Sets the significance level to use
sig
- the significance level
getSignificanceLevel
public double getSignificanceLevel()
Get the significance level
thresholdTipText
public java.lang.String thresholdTipText()
Returns the tip text for this property
setThreshold
public void setThreshold(double t)
Sets the threshold for comparisons
t
- the threshold to use
getThreshold
public double getThreshold()
Get the threshold
foldsTipText
public java.lang.String foldsTipText()
Returns the tip text for this property
setFoldsType
public void setFoldsType(SelectedTag d)
Set the xfold type
d
- the type of xval
getFoldsType
public SelectedTag getFoldsType()
Get the xfold type
debugTipText
public java.lang.String debugTipText()
Returns the tip text for this property
setDebug
public void setDebug(boolean d)
Set whether verbose output should be generated.
d
- true if output is to be verbose.
getDebug
public boolean getDebug()
Get whether output is to be verbose
attributeEvaluatorTipText
public java.lang.String attributeEvaluatorTipText()
Returns the tip text for this property
setAttributeEvaluator
public void setAttributeEvaluator(ASEvaluation newEvaluator)
Set the attribute evaluator to use for generating the ranking.
newEvaluator
- the attribute evaluator to use.
getAttributeEvaluator
public ASEvaluation getAttributeEvaluator()
Get the attribute evaluator used to generate the ranking.
generateRankingTipText
public java.lang.String generateRankingTipText()
Returns the tip text for this property
setGenerateRanking
public void setGenerateRanking(boolean doRank)
Records whether the user has requested a ranked list of attributes.
doRank
- true if ranking is requested
getGenerateRanking
public boolean getGenerateRanking()
Gets whether ranking has been requested. This is used by the
AttributeSelection module to determine if rankedAttributes()
should be called.
numToSelectTipText
public java.lang.String numToSelectTipText()
Returns the tip text for this property
setNumToSelect
public void setNumToSelect(int n)
Specify the number of attributes to select from the ranked list
(if generating a ranking). -1
indicates that all attributes are to be retained.
n
- the number of attributes to retain
getNumToSelect
public int getNumToSelect()
Gets the number of attributes to be retained.
getCalculatedNumToSelect
public int getCalculatedNumToSelect()
Gets the calculated number of attributes to retain. This is the
actual number of attributes to retain. This is the same as
getNumToSelect if the user specifies a number which is not less
than zero. Otherwise it should be the number of attributes in the
(potentially transformed) data.
selectionThresholdTipText
public java.lang.String selectionThresholdTipText()
Returns the tip text for this property
setSelectionThreshold
public void setSelectionThreshold(double threshold)
Set the threshold by which the AttributeSelection module can discard
attributes.
threshold
- the threshold.
getSelectionThreshold
public double getSelectionThreshold()
Returns the threshold so that the AttributeSelection module can
discard attributes from the ranking.
listOptions
public java.util.Enumeration listOptions()
Returns an enumeration describing the available options
setOptions
public void setOptions(java.lang.String options[]) throws java.lang.Exception
Parses a given list of options.
Valid options are:
0 = forward, 1 = backward, 2 = schemata, 3 = rank.
significance level to use for t-tests.
threshold for considering mean errors of two subsets the same
0 = 10 fold, 1 = leave-one-out (selected automatically for schemata race
the attribute evaluator to use when doing a rank search
produce a ranked list of attributes. Selecting this option forces
the race type to be forward. Racing continues until *all* attributes
have been selected, thus producing a ranked list of attributes.
Specify the number of attributes to retain. Overides any threshold.
Use in conjunction with -Q.
Specify a threshold by which the AttributeSelection module can discard
attributes. Use in conjunction with -Q.
Turn on verbose output for monitoring the search
options
- the list of options as an array of strings
getOptions
public java.lang.String[] getOptions()
Gets the current settings of BestFirst.
search
public int[] search(ASEvaluation ASEval,
Instances data) throws java.lang.Exception
Searches the attribute subset space by racing cross validation
errors of competing subsets
rankedAttributes
public double[][] rankedAttributes() throws java.lang.Exception
toString
public java.lang.String toString()
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