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java.lang.Objectweka.attributeSelection.ASSearch
weka.attributeSelection.SubsetSizeForwardSelection
public class SubsetSizeForwardSelection
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.
-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
Field Summary | |
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static Tag[] |
TAGS_TYPE
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Constructor Summary | |
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SubsetSizeForwardSelection()
Constructor |
Method Summary | |
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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 |
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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 |
Field Detail |
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public static final Tag[] TAGS_TYPE
Constructor Detail |
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public SubsetSizeForwardSelection()
Method Detail |
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public java.lang.String globalInfo()
public TechnicalInformation getTechnicalInformation()
public java.util.Enumeration listOptions()
listOptions
in interface OptionHandler
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-I
Perform initial ranking to select top-ranked attributes.
-K
-T <0 = fixed-set | 1 = fixed-width>
-S
-E
-R
-Z
Number of top-ranked attributes that are taken into account.
Typ of Linear Forward Selection (default = 0).
Size of lookup cache for evaluated subsets. Expressed as a multiple of
the number of attributes in the data set. (default = 1).
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).
Seed for cross validation subset size determination. (default = 1)
verbose on/off.
setOptions
in interface OptionHandler
options
- the list of options as an array of strings
java.lang.Exception
- if an option is not supported
public void setLookupCacheSize(int size)
size
- the maximum size of the hashtablepublic int getLookupCacheSize()
public java.lang.String lookupCacheSizeTipText()
public java.lang.String performRankingTipText()
public void setPerformRanking(boolean b)
b
- true if initial ranking should be performedpublic boolean getPerformRanking()
public java.lang.String numUsedAttributesTipText()
public void setNumUsedAttributes(int k) throws java.lang.Exception
k
- the number of attributes
java.lang.Exception
- if k is less than 2public int getNumUsedAttributes()
public java.lang.String typeTipText()
public void setType(SelectedTag t)
t
- the Linear Forward Selection typepublic SelectedTag getType()
public java.lang.String subsetSizeEvaluatorTipText()
public void setSubsetSizeEvaluator(ASEvaluation eval) throws java.lang.Exception
eval
- the subset evaluator to use for subset size determination.
java.lang.Exception
public ASEvaluation getSubsetSizeEvaluator()
public java.lang.String numSubsetSizeCVFoldsTipText()
public void setNumSubsetSizeCVFolds(int f)
f
- number of foldspublic int getNumSubsetSizeCVFolds()
public java.lang.String seedTipText()
public void setSeed(int s)
s
- seedpublic int getSeed()
public java.lang.String verboseTipText()
public void setVerbose(boolean b)
d
- true if output is to be verbose.public boolean getVerbose()
public java.lang.String[] getOptions()
getOptions
in interface OptionHandler
public java.lang.String toString()
toString
in class java.lang.Object
public int[] search(ASEvaluation ASEval, Instances data) throws java.lang.Exception
search
in class ASSearch
ASEvaluator
- the attribute evaluator to guide the searchdata
- the training instances.
java.lang.Exception
- if the search can't be completedpublic java.lang.String getRevision()
getRevision
in interface RevisionHandler
getRevision
in class ASSearch
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