Package weka.attributeSelection

Interface Summary

Interface	Description
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

Class	Description
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.
ClassifierAttributeEval	ClassifierAttributeEval : Evaluates the worth of an attribute by using a user-specified classifier. Valid options are:
ClassifierSubsetEval	Classifier subset evaluator: Evaluates attribute subsets on training data or a separate hold out testing set.
CorrelationAttributeEval	CorrelationAttributeEval : Evaluates the worth of an attribute by measuring the correlation (Pearson's) between it and the class. Nominal attributes are considered on a value by value basis by treating each value as an indicator.
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:
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:
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.
Ranker	Ranker : Ranks attributes by their individual evaluations.
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.
SymmetricalUncertAttributeEval	SymmetricalUncertAttributeEval : Evaluates the worth of an attribute by measuring the symmetrical uncertainty with respect to the class.
UnsupervisedAttributeEvaluator	Abstract unsupervised attribute evaluator.
UnsupervisedSubsetEvaluator	Abstract unsupervised attribute subset evaluator.
WrapperSubsetEval	WrapperSubsetEval: Evaluates attribute sets by using a learning scheme.