Rotational augmentation techniques: a new perspective on ensemble learning for image classification
This work addresses image classification accuracy for researchers, but it is incremental as it builds on existing rotational augmentation and ensemble methods.
The study tackled improving image classification accuracy by applying rotational augmentation to generate test sets and using ensemble voting systems, finding that ensemble-based voting outperformed simple voting.
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.