10 fold cross validation

how to use 10 fold cross validation in Multilayer extreme learning machine

Answer 1

[Demet]

Hello,

I have never used Multilayer extreme learning machine but i found this. The code below was written assuming that the code in this link is correct and It would be helpful for you

data= dlmread('data\\inputs1.txt'); %inputs
groups=dlmread('data\\targets1.txt'); % target
Fold=10;
indices = crossvalind('Kfold',length(groups),Fold);
for i =1:Fold
    testy = (indices == i);   
    trainy = (~testy);   
    TestInputData=data(testy,:)'; 
    TrainInputData=data(trainy,:)';
    TestOutputData=groups(testy,:)'; 
    TrainOutputData=groups(trainy,:)';
    
    
    number_neurons=[1000 100 100 100];% acchetecture of network
    NL=4;
    ELM_Type=1;
    
    [training_Acuracy]=MLP_elm_train(TrainInputData,TrainOutputData,number_neurons,ELM_Type,NL);%training
    training_Acuracy_f(fold)=training_Acuracy; %keep training acc for each fold
    
    [testing_Accuracy,output]=MLP_elm_predict(TestInputData, TestOutputData,ELM_Type,NL);%testing
    testing_Accuracy_f(Fold)=testing_Accuracy;% keep testing acc for each fold
end

[uma]

thank you so much.

Answer 2

[uma]

how we can specify the input and target data as i have a dataset namely segment attached here.