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Evaluation of Parameter Settings for Training Neural Networks Using Backpropagation Algorithms: A Study With Clinical Datasets by Leema N.; Khanna H. Nehemiah; Elgin Christo V. R.; Kannan A. is a Engineering article available to read on EtoBox.

What is Evaluation of Parameter Settings for Training Neural Networks Using Backpropagation Algorithms: A Study With Clinical Datasets about?

Artificial neural networks (ANN) are widely used for classification, and the training algorithm commonly used is the backpropagation (BP) algorithm. The major bottleneck faced in the backpropagation neural network training is in fixing the appropriate values for network parameters. The network parameters are initial weights, biases, activation function, number of hidden layers and the number of neurons per hidden layer, number of training epochs, learning rate, minimum error, and momentum term for the classification task. The objective of this work is to investigate the performance of 12 different BP algorithms with the impact of variations in network parameter values for the neural network training. The algorithms were evaluated with different training and testing samples taken from the three benchmark clinical datasets, namely, Pima Indian Diabetes (PID), Hepatitis, and Wisconsin Breast Cancer (WBC) dataset obtained from the University of California Irvine (UCI) machine learning repository.

Who reads Evaluation of Parameter Settings for Training Neural Networks Using Backpropagation Algorithms: A Study With Clinical Datasets?

Author

Leema N.; Khanna H. Nehemiah; Elgin Christo V. R.; Kannan A.

Publisher

IGI Global Publishing (ISSN 1947-9328)

Published

2020

Language

EN

Field

Engineering (Social Sciences)