Journal Article

iTabNet: an improved neural network for tabular data and its application to predict socioeconomic and environmental attributes

Liu, Yunxia, Li, Mengyao, Tian, Tian, Liu, Junmin, Hu, Sufeng

2023

Overview

There is a growing application of machine learning methods to predict socioeconomic and environmental attributes in computational social science, where big data are usually presented in tabular format. However, it is still a challenge to develop novel deep learning models to deal with tabular data, fill missing value, improve prediction accuracy, and enhance interpretability. In this study, we for the first time apply a tabular deep learning methodology (TabNet) to predict socioeconomic and environmental attributes (number of population and companies, volume of consumption, poker players’ behaviors, forest cover, etc.). Furthermore, we develop a new network architecture, referred to as improved TabNet (iTabNet), that can simultaneously learn local and global features in the tabular data to improve prediction accuracy. We also introduce a difference loss to constrain the feature selection process in iTabNet so that the model can use different features at different steps to enhance interpretability. To deal with missing values, we introduce a fusion strategy based on data mean and Auto-Encoder network to efficiently complete a more reasonable value filling. Experimental results demonstrate that the proposed iTabNet achieves competitive performances in the application to predict socioeconomic and environmental attributes based on tabular data, iTabNet using the proposed fusion strategy significantly outperforms other machine learning models when tabular data have missing values.

• Publisher: Springer London,Springer Nature B.V
• Subject: Accuracy / Artificial Intelligence / Big Data / Coders / Computational Biology/Bioinformatics / Computational Science and Engineering / Computer architecture / Computer Science / Data Mining and Knowledge Discovery / Deep learning / Image Processing and Computer Vision / Machine learning / Neural networks / Original Article / Probability and Statistics in Computer Science / Strategy / Tables (data)