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

Interface Summary

Interface	Description
IQualitativeMeasure	An Interface specifies the calculate method signature to be implemented by all qualitative measures

Class Summary

Class	Description
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)
APRF	This class is an abstract class for the Precision, Recall and F-Measure classes. It contains set of methods that calculate the values of true-positive, false-positive, true-negative and false-negative which are used by evaluators classes to evaluate the mappings results.
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.
AUC	Quantitative measure representing the area under the curve of ROC (see here).
FMeasure	F-Measure is the weighted average of the precision and recall
McNemarsTest	Implements McNemar's Test
Precision	It can be defined as the ratio of the retrieved correct results relative to the total number of the retrieved results,i.e.
PseudoFMeasure	Implements a quality measure for unsupervised ML algorihtms, dubbed pseudo F-Measure. Thereby, not relying on any gold standard.
PseudoPrecision	Implements a quality measure for unsupervised ML algorihtms, dubbed pseudo F-Measure.
PseudoRecall	Implements a quality measure for unsupervised ML algorihtms, dubbed pseudo F-Measure.
PseudoRefFMeasure	Implements a quality measure for unsupervised ML algorihtms, dubbed pseudo Reference F-Measure. Thereby, not relying on any gold standard.
PseudoRefPrecision	Implements a quality measure for unsupervised ML algorihtms, dubbed pseudo Reference Precision. Thereby, not relying on any gold standard.
PseudoRefRecall	Implements a quality measure for unsupervised ML algorihtms, dubbed pseudo Reference Recall. Thereby, not relying on any gold standard.
QualitativeMeasuresEvaluator	The class implements the evaluate method to evaluate mappings against several qualitative measures
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