Classification on Different Databases Using Artificial Neural Network (ANN) and 10-Fold Cross-Validation
In this paper, the author introduces a classification approach using multilayer perceptron with the backpropagation learning algorithm. This paper presents a comparative study on the performance of artificial neural networks on 3 different datasets. An artificial neural network with 10-fold cross validation was applied to UCI datasets – Iris, Wisconsin breast cancer and BUPA liver disorder datasets.
The performance measures (accuracy,sensitivity and specificity) were measured and the values obtained were compared to other studies on the same databases. 97.33% , 97.38% and 71.53% accuracies were obtained for Iris, Wisconsin breast cancer and BUPA liver disorder datasets respectively.
You can see the Confusion Matrixes below:
