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

Class Accuracy

java.lang.Object

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

org.aksw.limes.core.evaluation.qualititativeMeasures.Accuracy

All Implemented Interfaces:

IQualitativeMeasure

public class Accuracy
extends APRF
implements IQualitativeMeasure

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)

Since:

1.0

Version:

1.0

Author:

Mofeed Hassan (mounir@informatik.uni-leipzig.de), Tommaso Soru (tsoru@informatik.uni-leipzig.de)

Constructor Summary

Constructors

Constructor	Description
Accuracy()

Method Summary

Modifier and Type	Method	Description
double	calculate(AMapping predictions, GoldStandard goldStandard)	The method calculates the accuracy 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

Accuracy

public Accuracy()

Method Detail

calculate

public double calculate(AMapping predictions,
                        GoldStandard goldStandard)

The method calculates the accuracy 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 accuracy