Comparisons of MLP transfer functions for different classification classes

Abstract

This paper presents a comparison study of two different MLP transfer functions for three different classification cases of breast cancer, thyroid disease and weather classification. The transfer functions under investigation are sigmoid and hyperbolic tangent. In the study, MLP network was trained and tested to investigate the ability of the network to classify the breast cancer correctly between benign cell and malignant cell, classifying thyroid disease into normal, hyper or hypo thyroid and classifying weather conditions into four types; rain, cloudy, dry day and storm. Levenberg-Marquardt algorithm is adopted to train MLP network since it is the fastest training and ensure the best converges towards a minimum error. The performance of MLP networks was evaluated in terms of percentages for correct classification of the target outputs. Both functions are able to give accuracies up to 99% for classifying correctly. The hyperbolic tangent function had shown the capability of achieving the highest accuracy of an MLP performance with less number of hidden nodes.