parameter_combinations = grid_search(feautre_selection_parameters, classifier_parameters)
for i = 1:5
    data_test_cv_outer = data_whole{i}
    data_train_cv_outer = data_whole-data_test_cv_outer
    for k = 1:number_of_parameter_combinations
        for j = 1:5
            data_test_cv_inner = data_train_cv_outer{j}
            data_train_cv_inner = data_train_cv_outer-data_test_cv_inner
            feature_selected_inner = Fscore(data_train_cv_inner,feature_selection_parameters{k})  
            classifer_inner = classifier_construct(feature_selected_inner,classifier_parameters{k})
            label_predict_cv{j} = predict(classifer_inner,data_test_cv_inner)
         end
         acc_cv(k) = acc_calculate(label_actual_cv,label_predict_cv)
    end
    index_max_acc_cv = max_index(acc_cv)
    best_parameter = parameter_combinations{index_max_acc_cv)
    feature_selected_outer = Fscore(data_train_cv_outer,best_feature_selection_parameter)
    classifier_outer = classifier_construct(feature_selected_outer,best_classifier_parameter)
    label_predict{i} = predict(classifier_outer,data_test_cv_outer)
end
acc_final = acc_calculate(label_actual,label_predict)