In this paper, we propose a novel feature weighting method to address the
limitation of existing feature processing methods for tabular data. Typically
the existing methods assume equal importance across all samples and features in
one dataset. This simplified processing methods overlook the unique
contributions of each feature, and thus may miss important feature information.
As a result, it leads to suboptimal performance in complex datasets with rich
features. To address this problem, we introduce Tabular Feature Weighting with
Transformer, a novel feature weighting approach for tabular data. Our method
adopts Transformer to capture complex feature dependencies and contextually
assign appropriate weights to discrete and continuous features. Besides, we
employ a reinforcement learning strategy to further fine-tune the weighting
process. Our extensive experimental results across various real-world datasets
and diverse downstream tasks show the effectiveness of TFWT and highlight the
potential for enhancing feature weighting in tabular data analysis.

本文提出了一种新颖的特征加权方法来解决现有的表格数据特征处理方法的局限性。通过采用 Transformer 捕捉复杂的特征依赖关系并为离散和连续特征分配适当的权重，再结合强化学习策略对权重进行微调，我们的 TFWT 方法在各种真实数据集和不同下游任务上获得了令人满意的实验结果，凸显了在表格数据分析中增强特征加权的潜力。

TFWT：具有 Transformer 的表格特征权重

TFWT: Tabular Feature Weighting with Transformer

Long-range contextual information is essential for achieving high-performance
semantic segmentation. Previous feature re-weighting methods demonstrate that
using global context for re-weighting feature channels can effectively improve
the accuracy of semantic segmentation. However, the globally-sharing feature
re-weighting vector might not be optimal for regions of different classes in
the input image. In this paper, we propose a Context-adaptive Convolution
Network (CaC-Net) to predict a spatially-varying feature weighting vector for
each spatial location of the semantic feature maps. In CaC-Net, a set of
context-adaptive convolution kernels are predicted from the global contextual
information in a parameter-efficient manner. When used for convolution with the
semantic feature maps, the predicted convolutional kernels can generate the
spatially-varying feature weighting factors capturing both global and local
contextual information. Comprehensive experimental results show that our
CaC-Net achieves superior segmentation performance on three public datasets,
PASCAL Context, PASCAL VOC 2012 and ADE20K.

本文提出了一种上下文自适应卷积神经网络 (CaC-Net)，用于语义特征映射的空间变化特征加权。实验证明，CaC-Net 在语义分割任务中的性能优于其他方法。