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Adaptive Learning Rate For Neural Network Classification Model
Author(s)
Date Issued
January 1, 2022
Type
Conference Paper
Abstract
Imbalanced data cause prediction inaccuracy of the classification model. Two types of techniques have been devised to address this problem: pre-processing data before training a classification model and adjusting the classification algorithm. This study, which introduced the adaptive learning rate into a backpropagation neural network algorithm, is of the latter type. The learning rate was adjusted in each iterative learning cycle: the learning rate is increased for the data class with fewer samples and decreased for the data class with more samples. K-fold cross-validation was used to test the effectiveness of the prediction model on 10 datasets. The results showed that the proposed ZMP algorithm outperformed the original backpropagation neural network on 6 datasets; the improvement ranged from 2.24% to 20.22%. Moreover, on the other 4 datasets, even though the proposed technique provided less accurate predictions, the differences were very slight.
Citation
Icsec 2022 International Computer Science and Engineering Conference 2022, 177-181, 2022