# Rotational augmentation techniques: a new perspective on ensemble learning for image classification Source: [Rotational augmentation techniques: a new perspective on ensemble learning for image classification]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:26:24.585Z Authors: Unai Mu\\~noz-Aseguinolaza, Basilio Sierra, Naiara Aginako Published: 2023-06-12T11:04:11.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()