Deep learning methods have demonstrated outstanding performances on
classification and regression tasks on homogeneous data types (e.g., image,
audio, and text data). However, tabular data still poses a challenge with
classic machine learning approaches being often computationally cheaper and
equally effective than increasingly complex deep learning architectures. The
challenge arises from the fact that, in tabular data, the correlation among
features is weaker than the one from spatial or semantic relationships in
images or natural languages, and the dependency structures need to be modeled
without any prior information. In this work, we propose a novel deep learning
architecture that exploits the data structural organization through
topologically constrained network representations to gain spatial information
from sparse tabular data. The resulting model leverages the power of
convolutions and is centered on a limited number of concepts from network
topology to guarantee (i) a data-centric, deterministic building pipeline; (ii)
a high level of interpretability over the inference process; and (iii) an
adequate room for scalability. We test our model on 18 benchmark datasets
against 5 classic machine learning and 3 deep learning models demonstrating
that our approach reaches state-of-the-art performances on these challenging
datasets. The code to reproduce all our experiments is provided at
this https URL

通过在稀疏的表格数据中利用拓扑约束网络表示结构，我们提出了一种新颖的深度学习架构，利用卷积提取空间信息，并通过网络拓扑的有限概念来确保数据导向、可解释性和可扩展性，在 18 个基准数据集上测试表明，我们的方法在这些具有挑战性的数据集上达到了最先进的性能。