Adaptive Learning Rate for Dealing with Imbalanced Data in Classification Problems

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dc.contributor.authorJantanasukon, Ratanon
dc.contributor.authorThammano, Arit
dc.date.accessioned2026-08-06T10:32:09Z
dc.date.available2026-08-06T10:32:09Z
dc.date.issued2021-03-03
dc.description.abstractThis research modified a backpropagation learning algorithm in order to increase its ability to deal with imbalanced data problems. We used the backpropagation algorithm and a concept of multiple adaptive learning rates to train the feedforward neural network. Using multiple adaptive learning rates allowed us to achieve a classification model that had fewer problems when dealing with an imbalanced dataset the experimental results showed that the proposed method performed significantly better than the conventional backpropagation neural network in all tests.
dc.identifier.citation2021 Joint 6th International Conference on Digital Arts Media and Technology with 4th Ecti Northern Section Conference on Electrical Electronics Computer and Telecommunication Engineering Ecti Damt and Ncon 2021, 229-232, 2021
dc.identifier.doi10.1109/ECTIDAMTNCON51128.2021.9425715
dc.identifier.other2-s2.0-85106595325
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/11905
dc.source2021 Joint 6th International Conference on Digital Arts Media and Technology with 4th Ecti Northern Section Conference on Electrical Electronics Computer and Telecommunication Engineering Ecti Damt and Ncon 2021
dc.subjectAdaptive learning rate
dc.subjectBackpropagation algorithm
dc.subjectClassification
dc.subjectFeedforward neural network
dc.subjectImbalanced data
dc.titleAdaptive Learning Rate for Dealing with Imbalanced Data in Classification Problems
dc.typeConference Paper

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