Learning Enhanced Representations for Tabular Data via Neighborhood
Propagation
- URL: http://arxiv.org/abs/2206.06587v1
- Date: Tue, 14 Jun 2022 04:24:52 GMT
- Title: Learning Enhanced Representations for Tabular Data via Neighborhood
Propagation
- Authors: Kounianhua Du, Weinan Zhang, Ruiwen Zhou, Yangkun Wang, Xilong Zhao,
Jiarui Jin, Quan Gan, Zheng Zhang, David Wipf
- Abstract summary: We construct a hypergraph to model the cross-row and cross-column patterns of data instances.
We then perform message propagation to enhance the target data instance representation.
Experiments on two important data prediction tasks validate the superiority of the proposed PET model.
- Score: 24.485479610138498
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Prediction over tabular data is an essential and fundamental problem in many
important downstream tasks. However, existing methods either take a data
instance of the table independently as input or do not fully utilize the
multi-rows features and labels to directly change and enhance the target data
representations. In this paper, we propose to 1) construct a hypergraph from
relevant data instance retrieval to model the cross-row and cross-column
patterns of those instances, and 2) perform message Propagation to Enhance the
target data instance representation for Tabular prediction tasks. Specifically,
our specially-designed message propagation step benefits from 1) fusion of
label and features during propagation, and 2) locality-aware high-order feature
interactions. Experiments on two important tabular data prediction tasks
validate the superiority of the proposed PET model against other baselines.
Additionally, we demonstrate the effectiveness of the model components and the
feature enhancement ability of PET via various ablation studies and
visualizations. The code is included in https://github.com/KounianhuaDu/PET.
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