Improving the Imbalanced Data Accuracy Using CNN and ReLU

Abstract

In today’s academic world, learning from datasets is a trendy topic. In the event of imbalanced data, none of the many data mining tools available for this purpose work well, partly because this type of data generates a range of minority classes, which might obstruct the learning process. In addition to its enormous volume, big data has the traits of speed and variety. In this paper, the authors have proposed a CNN model with a rectified linear activation function (ReLU) activation function to get good accuracy on an imbalanced dataset. The dataset used in this paper was electrocardiogram (ECG) heartbeat categorization.