ISM: intra-class similarity mixing for time series augmentation

We propose a method based on intra-class similarity mixing, effectively alleviating the inability to improve the generalization of classification models. Its core concept involves improving the feature diversity and expanding the data scale by mixing local regions with similar features. The experimental results show that the proposed method can significantly outperform the similarity-based state-of-the-art methods. In future work, we will explore how to build and quantify the relationship between local similarity and lassification characteristics to further improve performance.