Multi-Label Learning Based on Double Laplace Regularization and Causal Inference

Label-specific features are a research hotspot in multi-label learning, which utilizes label feature extraction to solve the problem of multiple class labels in a single instance. Existing research on multi-label classification usually considers only the correlation between labels and ignores the local manifold structure between the original data, which results in a decrease in classification accuracy. In addition, in label correlation, the structural relationship between features and labels, as well as the inherent causal relationship between labels, are often overlooked. To address these issues, in this study, a multi-label learning algorithm based on double Laplace regularization and causal inference is proposed. Linear regression models are used to establish a basic multi-label classification framework which is combined with causal learning to explore the inherent causal relationships between labels, to achieve the goal of mining the essential connections between labels. To fully utilize the structural relationship between features and labels, double Laplace regularization is added to mine local label association information and effectively maintain the local manifold structure of the original data. The effectiveness of the proposed algorithm is verified on a public multi-label dataset. The experimental results showed that compared to algorithms such as LLSF, ML-KNN, and LIFT, the proposed algorithm achieved an average performance improvement of 8.82%, 4.98%, 9.43%, 16.27%, 12.19%, and 3.35% in terms of Hamming Loss(HL), Average Precision(AP), One Error(OE), Ranking Loss(RL), coverage, and AUC, respectively.