  Tabular data is the most commonly used form of data in industry. Gradient
Boosting Trees, Support Vector Machine, Random Forest, and Logistic Regression
are typically used for classification tasks on tabular data. DNN models using
categorical embeddings are also applied in this task, but all attempts thus far
have used one-dimensional embeddings. The recent work of Super Characters
method using two-dimensional word embeddings achieved the state of art result
in text classification tasks, showcasing the promise of this new approach. In
this paper, we propose the SuperTML method, which borrows the idea of Super
Characters method and two-dimensional embeddings to address the problem of
classification on tabular data. For each input of tabular data, the features
are first projected into two-dimensional embeddings like an image, and then
this image is fed into fine-tuned two-dimensional CNN models for
classification. Experimental results have shown that the proposed SuperTML
method had achieved state-of-the-art results on both large and small datasets.
