Package weka.attributeSelection

Interface Summary
AttributeEvaluator Interface for classes that evaluate attributes individually.
AttributeTransformer Abstract attribute transformer.
ErrorBasedMeritEvaluator Interface for evaluators that calculate the "merit" of attributes/subsets as the error of a learning scheme
RankedOutputSearch Interface for search methods capable of producing a ranked list of attributes.
StartSetHandler Interface for search methods capable of doing something sensible given a starting set of attributes.
SubsetEvaluator Interface for attribute subset evaluators.
 

Class Summary
ASEvaluation Abstract attribute selection evaluation class
ASSearch Abstract attribute selection search class.
AttributeSelection Attribute selection class.
AttributeSetEvaluator Abstract attribute set evaluator.
BestFirst BestFirst:

Searches the space of attribute subsets by greedy hillclimbing augmented with a backtracking facility.
CfsSubsetEval CfsSubsetEval :

Evaluates the worth of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them.

Subsets of features that are highly correlated with the class while having low intercorrelation are preferred.

For more information see:

M.
CheckAttributeSelection Class for examining the capabilities and finding problems with attribute selection schemes.
ChiSquaredAttributeEval ChiSquaredAttributeEval :

Evaluates the worth of an attribute by computing the value of the chi-squared statistic with respect to the class.

Valid options are:

ClassifierSubsetEval Classifier subset evaluator:

Evaluates attribute subsets on training data or a seperate hold out testing set.
ConsistencySubsetEval ConsistencySubsetEval :

Evaluates the worth of a subset of attributes by the level of consistency in the class values when the training instances are projected onto the subset of attributes.
CostSensitiveASEvaluation Abstract base class for cost-sensitive subset and attribute evaluators.
CostSensitiveAttributeEval A meta subset evaluator that makes its base subset evaluator cost-sensitive.
CostSensitiveSubsetEval A meta subset evaluator that makes its base subset evaluator cost-sensitive.
ExhaustiveSearch ExhaustiveSearch :

Performs an exhaustive search through the space of attribute subsets starting from the empty set of attrubutes.
FCBFSearch FCBF :

Feature selection method based on correlation measureand relevance&redundancy analysis.
FilteredAttributeEval Class for running an arbitrary attribute evaluator on data that has been passed through an arbitrary filter (note: filters that alter the order or number of attributes are not allowed).
FilteredSubsetEval Class for running an arbitrary subset evaluator on data that has been passed through an arbitrary filter (note: filters that alter the order or number of attributes are not allowed).
GainRatioAttributeEval GainRatioAttributeEval :

Evaluates the worth of an attribute by measuring the gain ratio with respect to the class.

GainR(Class, Attribute) = (H(Class) - H(Class | Attribute)) / H(Attribute).

Valid options are:

GeneticSearch GeneticSearch:

Performs a search using the simple genetic algorithm described in Goldberg (1989).

For more information see:

David E.
GreedyStepwise GreedyStepwise :

Performs a greedy forward or backward search through the space of attribute subsets.
HoldOutSubsetEvaluator Abstract attribute subset evaluator capable of evaluating subsets with respect to a data set that is distinct from that used to initialize/ train the subset evaluator.
InfoGainAttributeEval InfoGainAttributeEval :

Evaluates the worth of an attribute by measuring the information gain with respect to the class.

InfoGain(Class,Attribute) = H(Class) - H(Class | Attribute).

Valid options are:

LatentSemanticAnalysis Performs latent semantic analysis and transformation of the data.
LFSMethods  
LinearForwardSelection LinearForwardSelection:

Extension of BestFirst.
OneRAttributeEval OneRAttributeEval :

Evaluates the worth of an attribute by using the OneR classifier.

Valid options are:

PrincipalComponents Performs a principal components analysis and transformation of the data.
RaceSearch Races the cross validation error of competing attribute subsets.
RandomSearch RandomSearch :

Performs a Random search in the space of attribute subsets.
Ranker Ranker :

Ranks attributes by their individual evaluations.
RankSearch RankSearch :

Uses an attribute/subset evaluator to rank all attributes.
ReliefFAttributeEval ReliefFAttributeEval :

Evaluates the worth of an attribute by repeatedly sampling an instance and considering the value of the given attribute for the nearest instance of the same and different class.
ScatterSearchV1 Class for performing the Sequential Scatter Search.
SubsetSizeForwardSelection SubsetSizeForwardSelection:

Extension of LinearForwardSelection.
SVMAttributeEval SVMAttributeEval :

Evaluates the worth of an attribute by using an SVM classifier.
SymmetricalUncertAttributeEval SymmetricalUncertAttributeEval :

Evaluates the worth of an attribute by measuring the symmetrical uncertainty with respect to the class.
SymmetricalUncertAttributeSetEval SymmetricalUncertAttributeSetEval :

Evaluates the worth of a set attributes by measuring the symmetrical uncertainty with respect to another set of attributes.
UnsupervisedAttributeEvaluator Abstract unsupervised attribute evaluator.
UnsupervisedSubsetEvaluator Abstract unsupervised attribute subset evaluator.
WrapperSubsetEval WrapperSubsetEval:

Evaluates attribute sets by using a learning scheme.