How to apply majority voting for classification ensemble in Matlab?

phdcomputer Eng - 2022-04-02T10:02:10+00:00

Question: How to apply majority voting for classification ensemble in Matlab?

I have five classifiers SVM, random forest, naive Bayes, decision tree, KNN,I attached my Matlab code. I want to combine the results of these five classifiers on a dataset by using majority voting method and I want to consider all these classifiers have the same weight. because the number of the tests is calculated 5 so the output of each classifier is 5 labels(class labels in this example is 1 or 2). I'll be gratefull to have your opinions      clear all close all clc load data.mat; data=data; [n,m]=size(data); rows=(1:n); test_count=floor((1/6)*n); sum_ens=0;sum_result=0; test_rows=randsample(rows,test_count); train_rows=setdiff(rows,test_rows); test=data(test_rows,:); train=data(train_rows,:); xtest=test(:,1:m-1); ytest=test(:,m); xtrain=train(:,1:m-1); ytrain=train(:,m); %-----------svm------------------ svm=svm1(xtest,xtrain,ytrain); %-------------random forest--------------- rforest=randomforest(xtest,xtrain,ytrain); %-------------decision tree--------------- DT=DTree(xtest,xtrain,ytrain); %---------------bayesian--------------------- NBModel = NaiveBayes.fit(xtrain,ytrain, 'Distribution', 'kernel'); Pred = NBModel.predict(xtest); dt=Pred; %--------------KNN---------------- knnModel=fitcknn(xtrain,ytrain,'NumNeighbors',4); pred=knnModel.predict(xtest); sk=pred; how can I apply majority voting directly on these outputs of classifiers in Matlab?   Thanks very much

Related Questions

Expert Answer

Neeta Dsouza answered . 2025-11-20

• Assuming you have your five prediction arrays from your five different classifiers, and
• all prediction arrays have the same size = length(test_rows), and
• you have 2 classes: 1 & 2, you can do the following:

 

% First we concatenate all prediciton arrays into one big matrix. 
% Make sure that all prediction arrays are of the same type, I am assumming here that they
% are type double. I am also assuming that all prediction arrays are column vectors.

Prediction = [svm,rforest,DTree,dt,sk];
Final_decision = zeros(length(test_rows),1);
all_results = [1,2]; %possible outcomes
for row = 1:length(test_rows)
    election_array = zeros(1,2); 
    for col = 1:5 %your five different classifiers
       election_array(Prediction(row,col)) = ... 
           election_array(Prediction(row,col)) + 1;
    end 
    [~,I] = max(election_array);
    Final_decision(row) = all_results(I);
end

 

Not satisfied with the answer ?? ASK NOW