Adaptive Learning Rate for Dealing with Imbalanced Data in Classification Problems

Abstract

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.