Determining the optimal number of folds to use in a K-fold cross-validation: A neural network classification experiment

Abstract

A large dataset is needed to obtain a large learning set for a suitable classifier, while a large testing set is needed for a good estimate of the classifier’s performance (i.e. error probability). With a small dataset, after its random partitioning into learning and testing sets, both sets would end up consisting of smaller samples, which then becomes difficult to use when seeking to obtain a suitable classifier from the learning set and a good estimate of its performance from the testing set. The K-fold cross-validation approach has been every so often suggested to overcome the problem of not being able to obtain a suitable classifier and a good estimate of its performance. Thus, the objective of this study experiment was to determine the optimal number of folds to use in a K-fold cross-validation, and this was done in a simulation way using an artificial two-class normal mixture dataset with a total of 1000 samples and the resilient back propagation learning method over 10,000 training epochs, with and without early stopping applications during the training of the neural networks.