MATLAB: How to check SVM model

classification datamachine learningmulti-class svmStatistics and Machine Learning Toolbox

Hi everyone,
I need your help for my project.
I have already built an SVM model for classification with 4 labels. The SVM model worked very well. Accuracy classification reaches more than 90%.
However, when I want to check the model with new data ( new data = the original data through an AWGN channel having a 10 dB signal-to-noise ratio (SNR). The classification result is always less than 30% accuracy.
I don't know why despite trying so many ways. Pls help me!!!
untitled.jpg
untitled1.jpg
My code is as follows:
%% preparing data
load('mydata.mat') % including 200 observers and 120 features, 4 labels
output = grp2idx(Y);
rand_num = randperm(size(X,1));
% training data set 70%, test set 30%,
X_train = X(rand_num(1:round(0.7*length(rand_num))),:);
y_train = output(rand_num(1:round(0.7*length(rand_num))),:);
X_test = X(rand_num(round(0.7*length(rand_num))+1:end),:);
y_test = output(rand_num(round(0.7*length(rand_num))+1:end),:);
%% Train a classifier
% This code specifies all the classifier options and trains the classifier.
template = templateSVM(...
    'KernelFunction', 'linear', ...
    'PolynomialOrder', [], ...
    'KernelScale', 'auto', ...
    'BoxConstraint', 1, ...
    'Standardize', true)
Mdl = fitcecoc(...
    X_train, ...
    y_train, ...
    'Learners', template, ...
    'Coding', 'onevsall',...
    'OptimizeHyperparameters','auto',...
    'HyperparameterOptimizationOptions',...
    struct('AcquisitionFunctionName',...
    'expected-improvement-plus'));
%% Perform cross-validation
partitionedModel = crossval(Mdl, 'KFold', 10);
% Compute validation predictions
[validationPredictions, validationScores] = kfoldPredict(partitionedModel);
% Compute validation accuracy
validation_error = kfoldLoss(partitionedModel, 'LossFun', 'ClassifError'); % validation error
validationAccuracy = 1 - validation_error;
%% test model
oofLabel_n = predict(Mdl,X_test);
oofLabel_n = double(oofLabel_n); % chuyen tu categorical sang dang double
test_accuracy_for_iter = sum((oofLabel_n == y_test))/length(y_test)*100;
%% save model
saveCompactModel(Mdl,'mySVM');

Best Answer

If you want your classifier to perform well on data with Gaussian noise added, I suggest training it on your original data with Gaussian noise added. That is, create an "augmented" dataset and train on that.
Related Question