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.

翻译：近年来，数据增强技术在机器学习中的普及程度有所提高，因为它们能够从现有数据集中创建新样本。特别是旋转增强，通过旋转图像并将其用作训练的额外数据点，展现出了巨大的潜力。本研究提出了一种新方法，旨在提升分类方法的性能，其中测试集通过对原始数据集中的每张图像应用变换而生成。随后，实施基于集成的方法以确定从增强阶段获得的每个子集中最可靠的结果，从而对每张原始图像做出最终预测。研究结果表明，旋转增强技术可以显著提高标准分类模型的准确性；而投票方案的选择会对模型性能产生重大影响。总体而言，研究发现使用基于集成的投票系统比简单投票能产生更准确的结果。