Adaptive Learning Rate for Dealing with Imbalanced Data in Classification Problems

This 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...

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Bibliographic Details
Published in2021 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering pp. 229 - 232
Main Authors Jantanasukon, Ratanon, Thammano, Arit
Format Conference Proceeding
LanguageEnglish
Published IEEE 03.03.2021
Subjects
Adaptive learning
Adaptive learning rate
Backpropagation
Backpropagation algorithm
Backpropagation algorithms
Classification
Digital art
Distance measurement
Feedforward neural network
Imbalanced data
Media
Training
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DOI10.1109/ECTIDAMTNCON51128.2021.9425715

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Summary:This 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.
DOI:10.1109/ECTIDAMTNCON51128.2021.9425715