Yes, data augmentation can be applied to structured data, although it is more commonly associated with unstructured data like images and text. Structured data typically consists of organized information in tabular formats, such as databases or spreadsheets. The main goal of data augmentation in this context is to enhance the dataset's diversity to improve model training, while maintaining the relationships and integrity of the data.
One common method for augmenting structured data is by introducing small variations to existing entries. For example, in a dataset of customer transactions, you can generate new records by slightly changing the numerical values of existing transactions, such as modifying the purchase amounts within a certain percentage of the original value. This approach simulates different customer behaviors without introducing unrealistic data points. Another method is to create synthetic entries by combining attributes of existing records, such as mixing characteristics of different customer profiles to generate new, plausible entries.
Additionally, techniques like swapping values between similar rows or adding noise to certain numerical features can be effective. For instance, consider a dataset that includes demographic information, like age or income. You could randomly adjust these values slightly for a subset of rows to create a more extensive range of scenarios. However, it's crucial to keep the augmented data realistic and relevant to the original dataset to ensure that the resulting model remains accurate and robust. Ultimately, while data augmentation is less intuitive for structured data than for images or text, it can be an effective strategy to enhance model performance.