Azzera filtri
Azzera filtri

10 fold cross validation

11 visualizzazioni (ultimi 30 giorni)
uma
uma il 13 Apr 2022
Risposto: uma il 16 Giu 2022
how to use 10 fold cross validation in Multilayer extreme learning machine

Risposta accettata

Demet
Demet il 19 Apr 2022
Modificato: Demet il 19 Apr 2022
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

Più risposte (1)

uma
uma il 16 Giu 2022
how we can specify the input and target data as i have a dataset namely segment attached here.

Categorie

Scopri di più su Statistics and Machine Learning Toolbox in Help Center e File Exchange

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by