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function selectedFeatures = DISR(k, featureMatrix, classColumn)
%function selectedFeatures = DISR(k, featureMatrix, classColumn)
%
%Computers optimal features according to DISR algorithm from
%On the Use of variable "complementarity for feature selection"
%by P Meyer, G Bontempi (2006)
%
%Computes the top k features from
%a dataset featureMatrix with n training examples and m features
%with the classes held in classColumn.
%
%DISR - arg(Xi) max(sum(Xj mem XS)(SimRel(Xij,Y)))
%where SimRel = MI(Xij,Y) / H(Xij,Y)
totalFeatures = size(featureMatrix,2);
classMI = zeros(totalFeatures,1);
unselectedFeatures = ones(totalFeatures,1);
score = 0;
currentScore = 0;
innerScore = 0;
iMinus = 0;
answerFeatures = zeros(k,1);
highestMI = 0;
highestMICounter = 0;
currentHighestFeature = 0;
%create a matrix to hold the SRs of a feature pair.
%initialised to -1 as you can't get a negative SR.
featureSRMatrix = -(ones(k,totalFeatures));
for n = 1 : totalFeatures
classMI(n) = mi(featureMatrix(:,n),classColumn);
if classMI(n) > highestMI
highestMI = classMI(n);
highestMICounter = n;
end
end
answerFeatures(1) = highestMICounter;
unselectedFeatures(highestMICounter) = 0;
for i = 2 : k
score = 0;
currentHighestFeature = 0;
iMinus = i-1;
for j = 1 : totalFeatures
if unselectedFeatures(j) == 1
%DISR - arg(Xi) max(sum(Xj mem XS)(SimRel(Xij,Y)))
%where SimRel = MI(Xij,Y) / H(Xij,Y)
currentScore = 0;
for m = 1 : iMinus
if featureSRMatrix(m,j) == -1
unionedFeatures = joint([featureMatrix(:,answerFeatures(m)),featureMatrix(:,j)]);
tempUnionMI = mi(unionedFeatures,classColumn);
tempTripEntropy = h([unionedFeatures,classColumn]);
featureSRMatrix(m,j) = tempUnionMI/tempTripEntropy;
end
currentScore = currentScore + featureSRMatrix(m,j);
end
if (currentScore > score)
score = currentScore;
currentHighestFeature = j;
end
end
end
%now highest feature is selected in currentHighestFeature
%store it
unselectedFeatures(currentHighestFeature) = 0;
answerFeatures(i) = currentHighestFeature;
end
selectedFeatures = answerFeatures;
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