Can data augmentation be applied to structured data?

Yes, data augmentation can be applied to structured data, though the techniques differ significantly from those used for unstructured data like images or text. Structured data, such as tables with rows and columns representing entities and features, requires augmentation methods that preserve the inherent relationships and constraints within the data. Unlike image augmentation, which might involve transformations like rotation or cropping, structured data augmentation focuses on generating new synthetic samples or perturbing existing data while maintaining logical consistency. This approach helps improve model robustness, address class imbalances, and mitigate overfitting in machine learning tasks.

One common method for augmenting structured data is adding controlled noise to numerical features. For example, in a dataset containing customer age and income, you could apply Gaussian noise with a small standard deviation to these numerical values. This creates slightly varied samples without breaking realistic bounds (e.g., ensuring ages stay positive and incomes don’t become implausibly high). Another technique is synthetic minority oversampling (SMOTE), which generates new instances for underrepresented classes by interpolating between existing data points. If a fraud detection dataset has few fraud cases, SMOTE can create synthetic fraud samples by combining features of similar real fraud instances, preserving the statistical patterns of the original data.

Domain-specific transformations are also effective. For instance, in time-series sales data, you might augment records by applying seasonal adjustments (e.g., simulating holiday sales spikes) or shifting timestamps within valid windows. Categorical data can be augmented by swapping values within permissible categories—like replacing a product category with a similar one based on co-purchase statistics. Tools like CTGAN (Conditional Tabular GAN) use generative models to create synthetic tabular data that mirrors the original distribution. However, developers must validate augmented data to avoid introducing unrealistic combinations, such as a “height” value incompatible with a given “age” in medical data. Testing augmented data with domain rules or statistical checks ensures consistency before training models.