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

Class FMeasure

java.lang.Object

org.aksw.limes.core.evaluation.qualititativeMeasures.APRF

org.aksw.limes.core.evaluation.qualititativeMeasures.FMeasure

All Implemented Interfaces:

IQualitativeMeasure

public class FMeasure
extends APRF
implements IQualitativeMeasure

F-Measure is the weighted average of the precision and recall

Since:

1.0

Version:

1.0

Author:

Tommaso Soru (tsoru@informatik.uni-leipzig.de)

Constructor Summary

Constructors

Constructor	Description
FMeasure()

Method Summary

Modifier and Type	Method	Description
double	calculate(AMapping predictions, GoldStandard goldStandard)	The method calculates the F-Measure of the machine learning predictions compared to a gold standard
double	calculate(AMapping predictions, GoldStandard goldStandard, double beta)	The method calculates the F-Measure of the machine learning predictions compared to a gold standard
double	precision(AMapping predictions, GoldStandard goldStandard)	The method calculates the precision of the machine learning predictions compared to a gold standard
double	recall(AMapping predictions, GoldStandard goldStandard)	The method calculates the recall of the machine learning predictions compared to a gold standard

Methods inherited from class org.aksw.limes.core.evaluation.qualititativeMeasures.APRF

falseNegative, trueFalsePositive, trueNegative

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

FMeasure

public FMeasure()

Method Detail

calculate

public double calculate(AMapping predictions,
                        GoldStandard goldStandard)

The method calculates the F-Measure of the machine learning predictions compared to a gold standard

Specified by:

calculate in interface IQualitativeMeasure

Specified by:

calculate in class APRF

Parameters:

predictions - The predictions provided by a machine learning algorithm

goldStandard - It contains the gold standard (reference mapping) combined with the source and target URIs

Returns:

double - This returns the calculated F-Measure

calculate

public double calculate(AMapping predictions,
                        GoldStandard goldStandard,
                        double beta)

The method calculates the F-Measure of the machine learning predictions compared to a gold standard

Parameters:

predictions - The predictions provided by a machine learning algorithm

goldStandard - It contains the gold standard (reference mapping) combined with the source and target URIs

Returns:

double - This returns the calculated F-Measure

recall

public double recall(AMapping predictions,
                     GoldStandard goldStandard)

The method calculates the recall of the machine learning predictions compared to a gold standard

Parameters:

predictions - The predictions provided by a machine learning algorithm

goldStandard - It contains the gold standard (reference mapping) combined with the source and target URIs

Returns:

double - This returns the calculated recall

precision

public double precision(AMapping predictions,
                        GoldStandard goldStandard)

The method calculates the precision of the machine learning predictions compared to a gold standard

Parameters:

predictions - The predictions provided by a machine learning algorithm

goldStandard - It contains the gold standard (reference mapping) combined with the source and target URIs

Returns:

double - This returns the calculated precision