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Backpropagation Neural Network with Adaptive Learning Rate for Classification
Author(s)
Jullapak, Rujira
Date Issued
January 1, 2023
Type
Book Chapter
Abstract
This research aims to improve the classification accuracy by modifying an original backpropagation neural network. In the proposed BPNN-ZMP, the learning rates were automatic tuned to improve the classification accuracy. Breast Cancer Coimbra dataset and Banknote Authentication dataset were used for testing the model performances. The results demonstrate that BPNN-ZMP improved over the original backpropagation neural network by 12.12 and 11.46% for Breast Cancer Coimbra dataset and Banknote Authentication dataset respectively. Although BPNN-ZMP could improve the model accuracy, the high accuracy in neural network backpropagation has been challenged in future work.
Citation
Lecture Notes on Data Engineering and Communications Technologies, 153, 493-499, 2023