Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation

Abstract

The data in the real world consists of various kinds of painful features. A majorly found one is the class imbalance in which the number of examples in different classes in a dataset is unequal. The class imbalance is being resolved using various sampling techniques on the data. Augmentation technique Augmentation is one of the essential steps in any machine learning pipeline and is used for oversampling the data of minority classes. This paper aims to improve the model performance is being enhanced with removing the class imbalance problem by using various Augmentation approaches to generate various balanced augmented datasets using various data augmentation techniques & random sampling. The accuracies are acquired for each augmentation technique using a RESNET18 model. The model is run up to 100 epochs for each case, and the best accuracies are compared. This iterative comparison of various augmentation techniques has shown stunning insights into the effectiveness of multiple datasets.