DANets: Deep Abstract Networks for Tabular Data Classification and Regression

Authors

  • Jintai Chen Zhejiang University
  • Kuanlun Liao Zhejiang University
  • Yao Wan Huazhong University of Science and Technology
  • Danny Z. Chen University of Notre Dame
  • Jian Wu Zhejiang University

DOI:

https://doi.org/10.1609/aaai.v36i4.20309

Keywords:

Data Mining & Knowledge Management (DMKM)

Abstract

Tabular data are ubiquitous in real world applications. Although many commonly-used neural components (e.g., convolution) and extensible neural networks (e.g., ResNet) have been developed by the machine learning community, few of them were effective for tabular data and few designs were adequately tailored for tabular data structures. In this paper, we propose a novel and flexible neural component for tabular data, called Abstract Layer (AbstLay), which learns to explicitly group correlative input features and generate higher-level features for semantics abstraction. Also, we design a structure re-parameterization method to compress the trained AbstLay, thus reducing the computational complexity by a clear margin in the reference phase. A special basic block is built using AbstLays, and we construct a family of Deep Abstract Networks (DANets) for tabular data classification and regression by stacking such blocks. In DANets, a special shortcut path is introduced to fetch information from raw tabular features, assisting feature interactions across different levels. Comprehensive experiments on seven real-world tabular datasets show that our AbstLay and DANets are effective for tabular data classification and regression, and the computational complexity is superior to competitive methods. Besides, we evaluate the performance gains of DANet as it goes deep, verifying the extendibility of our method. Our code is available at https://github.com/WhatAShot/DANet.

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Published

2022-06-28

How to Cite

Chen, J., Liao, K., Wan, Y., Chen, D. Z., & Wu, J. (2022). DANets: Deep Abstract Networks for Tabular Data Classification and Regression. Proceedings of the AAAI Conference on Artificial Intelligence, 36(4), 3930-3938. https://doi.org/10.1609/aaai.v36i4.20309

Issue

Section

AAAI Technical Track on Data Mining and Knowledge Management