CNN-Based Approaches for Various Types of Tabular Data
Vu Tuan Anh, Il Do Ha, Kevin F Burke · IEEE Access · 2025
Deep learning (DL) includes various architectures, such as deep neural networks (DNN) and convolutional neural networks (CNN). DL is very powerful and flexible for non-tabular (non-structured) data (e.g. image, text). However, in tabular data, standard DNNs often do not outperform traditional machine learning (ML) methods such as tree-based models (e.g. random forest, XGBoost). CNNs carry out dimensionality reduction for non-tabular (especially image) data, but may be useful in tabular data too. In this paper, we present a reformulation of one-dimensional CNN (1D-CNN)-based approaches for various types of tabular data, which provides an end-to-end learning framework. The predictive performance of the proposed method is evaluated by comparing it with existing ML/DL methods using four types of real tabular data, i.e. a binary response data with high dimensional features, over-dispersed count data, high-dimension survival data, and time-series data with substantial variability.