Uses of Class
org.aksw.limes.core.evaluation.qualititativeMeasures.APRF

Packages that use APRF

Package	Description
org.aksw.limes.core.evaluation.qualititativeMeasures

Uses of APRF in org.aksw.limes.core.evaluation.qualititativeMeasures

Subclasses of APRF in org.aksw.limes.core.evaluation.qualititativeMeasures

Modifier and Type	Class and Description
class	Accuracy The class represents the accuracy of the mapping which is defined as the proportion of true results (positive or negative) to the total number of the population, (T+) + (T-)/(+) + (-)), T+: true positive, T-:True negative(mxn-goldstandard-F+), +: all postitive (gold standard), -: all possible links out of gold standard(mxn-gold)
class	APseudoPRF This class is an abstract class for the Pseudo Precision, Pseudo Recall and Pseudo F-Measure classes. It extends the abstract class PRF and implements additional methods that sets, gets and checks some flags values required for pseudo-measures.
class	AUC Quantitative measure representing the area under the curve of ROC (see here).
class	FMeasure F-Measure is the weighted average of the precision and recall
class	Precision It can be defined as the ratio of the retrieved correct results relative to the total number of the retrieved results,i.e.
class	PseudoFMeasure Implements a quality measure for unsupervised ML algorihtms, dubbed pseudo F-Measure. Thereby, not relying on any gold standard.
class	PseudoPrecision Implements a quality measure for unsupervised ML algorihtms, dubbed pseudo F-Measure.
class	PseudoRecall Implements a quality measure for unsupervised ML algorihtms, dubbed pseudo F-Measure.
class	PseudoRefFMeasure Implements a quality measure for unsupervised ML algorihtms, dubbed pseudo Reference F-Measure. Thereby, not relying on any gold standard.
class	PseudoRefPrecision Implements a quality measure for unsupervised ML algorihtms, dubbed pseudo Reference Precision. Thereby, not relying on any gold standard.
class	PseudoRefRecall Implements a quality measure for unsupervised ML algorihtms, dubbed pseudo Reference Recall. Thereby, not relying on any gold standard.
class	Recall It measures how far the algorithm retrieved correct results out of the all existed correct results. It is defined to be the ratio between the true positive to the total number of correct results whether retrieved or not