Adaptive Learning Rate For Neural Network Classification Model

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dc.contributor.authorJullapak, Rujira
dc.contributor.authorThammano, Arit
dc.contributor.authorSurakratanasakul, Boonprasert
dc.date.accessioned2026-08-06T10:34:20Z
dc.date.available2026-08-06T10:34:20Z
dc.date.issued2022-01-01
dc.description.abstractImbalanced 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.
dc.identifier.citationIcsec 2022 International Computer Science and Engineering Conference 2022, 177-181, 2022
dc.identifier.doi10.1109/ICSEC56337.2022.10049365
dc.identifier.other2-s2.0-85149658261
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/12496
dc.sourceIcsec 2022 International Computer Science and Engineering Conference 2022
dc.subjectAdaptive Learning Rate
dc.subjectBackpropagation Neural Network
dc.subjectClassification
dc.titleAdaptive Learning Rate For Neural Network Classification Model
dc.typeConference Paper

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