How to apply majority voting for classification ensemble in Matlab?

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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
load data.mat;
%-------------random forest---------------
%-------------decision tree---------------
NBModel =,ytrain, 'Distribution', 'kernel');
Pred = NBModel.predict(xtest);
how can I apply majority voting directly on these outputs of classifiers in Matlab?
Thanks very much

Accepted Answer

Ahmad Obeid
Ahmad Obeid on 24 May 2019
Edited: Ahmad Obeid on 21 Oct 2019
I don't think that there's an existing function that does that for you, so you have to build your own. Here is a suggested method:
  • 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;
[~,I] = max(election_array);
Final_decision(row) = all_results(I);
Hope this helps.
doaa khalil
doaa khalil on 12 Aug 2020
hi Mr. Ahmed i want to apply majority voting for two classification model .Can you help me?
imds = imageDatastore('Preprocessing', ...
'IncludeSubfolders',true, ...
%Use countEachLabel to summarize the number of images per category.
tbl1 = countEachLabel(imds)
%Divide the data into training and validation data sets
rng('default') % For reproduciblity
[trainingSet, testSet] = splitEachLabel(imds, 0.3, 'randomize');
%Load Pretrained Network1
net1 = alexnet;
% Inspect the first layer
% Create augmentedImageDatastore from training and test sets to resize
% images in imds to the size required by the network1.
imageSize1 = net1.Layers(1).InputSize;
augmentedTrainingSet1 = augmentedImageDatastore(imageSize1, trainingSet, 'ColorPreprocessing', 'gray2rgb');
augmentedTestSet1 = augmentedImageDatastore(imageSize1, testSet, 'ColorPreprocessing', 'gray2rgb');
featureLayer1 = 'fc7';
trainingFeatures1 = activations(net1, augmentedTrainingSet1, featureLayer1, ...
'MiniBatchSize', 32, 'OutputAs', 'columns');
%Load Pretrained Network2
net2 = resnet50;
% Visualize the first section of the network.
title('First section of ResNet-50')
set(gca,'YLim',[150 170]);
% Inspect the first layer
% Create augmentedImageDatastore from training and test sets to resize
% images in imds to the size required by the network2.
imageSize2 = net2.Layers(1).InputSize;
augmentedTrainingSet2 = augmentedImageDatastore(imageSize2, trainingSet, 'ColorPreprocessing', 'gray2rgb');
augmentedTestSet2 = augmentedImageDatastore(imageSize2, testSet, 'ColorPreprocessing', 'gray2rgb');
featureLayer2 = 'fc1000';
trainingFeatures2 = activations(net2, augmentedTrainingSet2, featureLayer2, ...
'MiniBatchSize', 32, 'OutputAs', 'columns');
%% %% Train A Multiclass SVM Classifier Using CNN1 Features
% Get training labels from the trainingSet
trainingLabels = trainingSet.Labels;
% Train multiclass SVM classifier using a fast linear solver, and set
% 'ObservationsIn' to 'columns' to match the arrangement used for training
% features.
classifier1 = fitcecoc(newfeatures, trainingLabels, ...
'Learners', 'Linear', 'Coding', 'onevsall', 'ObservationsIn', 'columns');
% Extract test features using the CNN1
testFeatures1 = activations(net1, augmentedTestSet1, featureLayer1, ...
'MiniBatchSize', 32, 'OutputAs', 'columns');
% Pass CNN image features to trained classifier
predictedLabels1 = predict(classifier1, testFeatures1, 'ObservationsIn', 'columns');

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More Answers (3)

Li Ai
Li Ai on 30 Mar 2021
I think just put the outputs of five models together as a matrix, then use mode function

Abida Ashraf
Abida Ashraf on 16 Oct 2019
How three classifer like fitcnb,fitcecoc and fitensemble can be used to get average results.

Sinan Islam
Sinan Islam on 12 Dec 2020
Matlab should consider adding vote ensemble.

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