Solved – 10 fold cross validation model in weka

Question

I'm trying to build a specific neural network architecture and testing it using 10 fold cross validation of a dataset. Now building the model is a tedious job and Weka expects me to make it 10 times for each of the 10 folds.

Can't I just make the model for the first fold and ask weka to use that same model for the remaining 9?

Also, since building neural networks in weka is so easy, can I import a neural network structure to matlab for further use?

Answer 1

You can use the Evaluate class to perform this 10-fold cross-validation. To define the cross-validation you have to set the parameter as '-x 10' in the EvaluateModel.

clear all; close all; clc;

%% Add jar file to path plus import dependencies
javaaddpath('/usr/local/weka-3-6-11/weka.jar');
import weka.classifiers.trees.RandomForest.*;
import weka.classifiers.meta.Bagging.*;
import weka.classifiers.Evaluation.*;
import weka.core.Instances.*

%% load the arff file and extract the informations
filename = 'algo_output/results_features_labeling2_2class.arff';
loader = weka.core.converters.ArffLoader();
loader.setFile(java.io.File(filename));
data = loader.getDataSet();
data.setClassIndex(D.numAttributes()-1);

%% classification
classifier = weka.classifiers.functions.MultilayerPerceptron();
classifier.buildClassifier(data);
classifier.toString()

%% 10-fold cross-validation
ev = weka.classifiers.Evaluation(data);

v(1) = java.lang.String('-t');
v(2) = java.lang.String(filename);
v(3) = java.lang.String('-x');
v(4) = java.lang.String('10');
v(5) = java.lang.String('-i');

prm = cat(1,v(1:end));
ev.evaluateModel(classifier, prm)

For more information, check this link.