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function selectedFeatures = CMIM(k, featureMatrix, classColumn)
%function selectedFeatures = CMIM(k, featureMatrix, classColumn)
%Computes conditional mutual information maximisation algorithm from
%"Fast Binary Feature Selection with Conditional Mutual Information"
%by F. Fleuret (2004)
%Computes the top k features from
%a dataset featureMatrix with n training examples and m features
%with the classes held in classColumn.
noOfTraining = size(classColumn,1);
noOfFeatures = size(featureMatrix,2);
partialScore = zeros(noOfFeatures,1);
m = zeros(noOfFeatures,1);
score = 0;
answerFeatures = zeros(k,1);
highestMI = 0;
highestMICounter = 0;
for n = 1 : noOfFeatures
partialScore(n) = mi(featureMatrix(:,n),classColumn);
if partialScore(n) > highestMI
highestMI = partialScore(n);
highestMICounter = n;
end
end
answerFeatures(1) = highestMICounter;
for i = 2 : k
score = 0;
limitI = i - 1;
for n = 1 : noOfFeatures
while ((partialScore(n) >= score) && (m(n) < limitI))
m(n) = m(n) + 1;
conditionalInfo = cmi(featureMatrix(:,n),classColumn,featureMatrix(:,answerFeatures(m(n))));
if partialScore(n) > conditionalInfo
partialScore(n) = conditionalInfo;
end
end
if partialScore(n) >= score
score = partialScore(n);
answerFeatures(i) = n;
end
end
end
selectedFeatures = answerFeatures;
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