Related papers: Learning Enhanced Representations for Tabular Data via Neighborhood Propagation

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.