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

Baohua Sun,Lin Yang,Wenhan Zhang,Michael Lin,Patrick Dong,Charles Young,Jason Dong

Published 2019 in Unknown venue

ABSTRACT

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.

PUBLICATION RECORD

  • Publication year

    2019

  • Venue

    Unknown venue

  • Publication date

    2019-02-26

  • Fields of study

    Computer Science

  • Identifiers
    arXiv 1903.06246
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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