Abstract: Tabular data is ubiquitous in industrial applications,spanning fields such as medicine,finance,and manufacturing,where each sample is characterized by heterogeneous features.Multi-label classification for tabular data is crucial for capturing the complex,interconnected nature of real-world phenomena,yet obtaining large-scale labeled datasets is often costly.While semi-supervised learning has shown success in image and text data by leveraging unlabeled samples,its application to tabular data remains challenging due to the lack of inherent spatial or semantic structures,making conventional augmentation and consistency-based methods less effective.To address these challenges,this paper proposes a novel semi-supervised learning frameworktai-lored for multi-label tabular data.This approach introduces a structure-preserving data augmentation method that adds Gaussian noise to the feature representation space preserving the original data structure,and a consistency-based regularization technique between samples and their perturbed versions to enhance generalization.Additionally,an attention-based mechanism is developed to selectively aggregate neighborhood information from labeled data,allowing the model to leverage local feature correlations effectively.For unlabeled data,a state-of-the-art pseudo-labeling strategy is employed to enable iterative refinement of model predictions.Extensive experiments are conducted on ten public multi-label tabular datasets,covering various domains to validate the robustness of the proposed method.Results demonstrate the effectiveness of the proposed method,advancing the state of semi-supervised multi-label learning for tabular data.

Key words: Tabular data, Multi-label classification, Semi-supervised learning, Data augmentation, Attention mechanism