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

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 param...

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Veröffentlicht in:International journal of operations research and information systems Jg. 11; H. 4; S. 62 - 85
Hauptverfasser: Leema N, Nehemiah, Khanna H, Elgin Christo V. R, Kannan A
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hershey IGI Global 01.10.2020
Schlagworte:
Accuracy
Algorithms
Artificial neural networks
Back propagation
Back propagation networks
Classification
Datasets
Information storage
Information systems
Machine learning
Nervous system
Neural networks
Neurons
Open access publishing
Parameters
Pattern recognition
Signal transduction
Wisconsin
India
ISSN:1947-9328, 1947-9336
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1947-9328
1947-9336
DOI:10.4018/IJORIS.2020100104