Rotational augmentation techniques: a new perspective on ensemble
  learning for image classification
Unai Muñoz-Aseguinolaza Basilio Sierra Naiara Aginako
The popularity of data augmentation techniques in machine learning has
increased in recent years, as they enable the creation of new samples from
existing datasets. Rotational augmentation, in particular, has shown great
promise by revolving images and utilising them as additional data points for
training. This research study introduces a new approach to enhance the
performance of classification methods where the testing sets were generated
employing transformations on every image from the original dataset.
Subsequently, ensemble-based systems were implemented to determine the most
reliable outcome in each subset acquired from the augmentation phase to get a
final prediction for every original image. The findings of this study suggest
that rotational augmentation techniques can significantly improve the accuracy
of standard classification models; and the selection of a voting scheme can
considerably impact the model's performance. Overall, the study found that
using an ensemble-based voting system produced more accurate results than
simple voting.