SuperTML: Two-Dimensional Word Embedding and Transfer Learning Using ImageNet Pretrained CNN Models for the Classifications on Tabular DataDownload PDF

28 May 2019 (modified: 22 Oct 2023)Submitted to ICML Deep Phenomena 2019Readers: Everyone
Keywords: Structured Tabular Data, Two-dimensional Word Embedding, Transfer Learning
TL;DR: Deep learning for structured tabular data machine learning using pre-trained CNN model from ImageNet.
Abstract: Structured tabular data is the most commonly used form of data in industry according to a Kaggle ML and DS Survey. Gradient Boosting Trees, Support Vector Machine, Random Forest, and Logistic Regression are typically used for classification tasks on tabular data. The recent work of Super Characters method using two-dimensional word embedding achieved state-of-the-art results 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 embedding to address the problem of classification on tabular data. For each input of tabular data, the features are first projected into two-dimensional embedding like an image, and then this image is fed into fine-tuned ImageNet CNN models for classification. Experimental results have shown that the proposed SuperTML method have achieved state-of-the-art results on both large and small datasets.
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 3 code implementations](https://www.catalyzex.com/paper/arxiv:1903.06246/code)
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