The use of images for tabular classification is discussed extensively in the time series analysis group here:
There is a whole library here dedicated to time series to image transformations:
https://pyts.readthedocs.io/en/latest/auto_examples/index.html#imaging-time-series
I’m pretty skeptical of the author’s approach because they don’t provide enough details to replicate the image creation process and they don’t provide code. They elude to the fact they use feature importance to govern font size used in the image creation but I don’t see any way to reproduce their results from their paper.
Considering the transformation of tabular data to images is the key innovation in the paper, this is the only discussion of how this is done:
Algorithm 2 SuperTML EF: SuperTML method with Equal Fontsize for embedding.
Input: Tabular data training set
Parameter: Imagesize of the generated SuperTML images
Output: Finetuned CNN model
1: for each sample in the tabular data do
2: for each feature of the sample do
3: Draw the feature in the same fontsize without overlapping, such that the total features of the sample will occupy the imagesize as much as possible.
4: end for
5: end for