FFICL-Net: A Fusing Symmetric Feature-Importance Ranking Contrastive-Learning Network for Multivariate Time-Series Classification

Supervised contrastive learning has emerged as a novel method to help solve the problem of multivariate time-series classification. By utilizing labeled data, it maximally learns the feature-representation differences between various categories. However, existing supervised contrastive-learning approaches lack model interpretability, making it difficult to analyze the importance ranking among features. Experimentally, different preprocessing of the data often results in variations in feature-importance ranking. Therefore, we propose FFICL-Net, which combines LSTM, to analyze the importance of sequence variables, and ITransformer, to treat each variable as a token, learning the self-attention relationships between variables and their relationship to the final feature representation. This approach contrasts the feature importance derived from the two modules, making two feature-importance ranking results more similar and forming a kind of symmetry, allowing the resultant feature representation to fuse the characteristics of both models, leading to more stable and accurate feature-importance ranking results and aiding in improving classification accuracy. We conducted comparative experiments on all 30 public UEA datasets and achieved the best results on most of these datasets compared to the current top models. The average accuracy reached 72.8%, which is an improvement of 0.7% over the best-performing model.