Combining N pattern classifiers using weighted majority voting in Matlab

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daniel osuto
daniel osuto 2015-10-19
编辑： kh rezaee 2020-1-29
I want to combine some classifiers. The number of classifiers is 4 and there are 3 possible classes. I came a cross this code: "Efficient multiclass weighted majority voting implementation in MATLAB". It makes use of 3 classifiers and 3 possible classes. I have tried to customize it for use with 4 classifiers and 3 possible classes without success. How can this code be extended for use in my case, or to N classifiers. Or is there any other code applicable to my case. Please help.

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kh rezaee
kh rezaee 2020-1-29
编辑：kh rezaee 2020-1-29
I think that your problem is near this code:
voteWeightsSUM = sum(voteWeights);
W = voteWeights/(voteWeightsSUM);
outPut = (testPredictions(:,1)*W(1)+testPredictions(:,2)*W(2)+testPredictions(:,3)*W(3)+testPredictions(:,4)*W(4));
VotingConfusionMatrix = confusionmat(TestLabel,outPut);
softVotingAccuracy = sum(diag(VotingConfusionMatrix))/sum(VotingConfusionMatrix(:));
Where, voteWeights and testPredictions are accuracy and predicted test labels of each classifier, respectively. Also, voteWeights comes from the training phase, but testPredictions is calculated based on trained models.

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