I am working on a classification task for tabular data, and I am wondering if I should try a convolutional neural network or stick to a simple multi-layer perceptron. I usually see CNNs applied to images, but is there any benefit to using them on structured data, or is that just overkill? I am looking for advice on architecture selection for non-image datasets.
For tabular data, use a multi-layer perceptron for general classification tasks, reserving convolutional neural networks only for inputs that possess a significant, non-random spatial or temporal ordering.
4 answers
Before you commit to a specific architecture for your structured classification project, consider these criteria to ensure you are not adding technical debt without a clear performance justification.
- Use an MLP if your data has no inherent ordering and your priority is rapid deployment and interpretability.
- Consider a 1D CNN only if your input features possess a logical local sequence or periodic structure that a filter could extract.
- Validate your baseline performance with a gradient boosted tree model before experimenting with deep neural network architectures.
Stick with an MLP for tabular data because the overhead of a CNN is rarely worth the marginal gains in a business environment. Unless your columns have a spatial or temporal hierarchy that implies a sliding window relationship, a standard dense network will be easier to govern and explain to stakeholders.
I remember trying to force a 1D-CNN onto a financial dataset a few years back because I read a paper claiming it captured better local feature interactions. It took me three weeks to tune, and when I finally compared the lift against a tuned XGBoost and a simple MLP, the difference was negligible.
We ended up binning the CNN approach entirely because our support team couldn't maintain it when it inevitably drifted. Don't fall for the complexity trap if a simpler model gives you 98 percent of the performance with ten percent of the maintenance burden.
The choice between these architectures rests on whether you believe local correlations between adjacent features contain predictive information. An MLP treats all input features as independent units in a fully connected graph, whereas a CNN assumes that features are ordered such that neighbors are more relevant to one another than distant inputs. If your data consists of disparate metrics, like age, income, and account balance, there is no spatial relationship to exploit, making the convolutional approach fundamentally misaligned with the nature of the data. Conversely, if your tabular data represents a time-series signal or a structured sensor log, the CNN might capture recurring patterns that a dense layer would ignore. In most enterprise contexts, feature engineering on an MLP will consistently outperform the architectural gymnastics required to make a CNN work on flat, non-topological data.
