Adaptive Learning Rate for Dealing with Imbalanced Data in Classification Problems

• Published in: 2021 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
• Language: English
• 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
• DOI: 10.1109/ECTIDAMTNCON51128.2021.9425715

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.