ISM:intra-class similarity mixing for time series augmentation  

作　　者：Pin LIU Rui WANG Yongqiang HE Yuzhu WANG 

机构地区：[1]School of Information Engineering,China University of Geosciences,Beijing 100083,China [2]State Key Lab of Software Development Environment,Beihang University,Beijing 100191,China

出　　处：《Frontiers of Computer Science》2024年第6期273-275,共3页计算机科学前沿（英文版）

基　　金：the Fundamental Research Funds for the Central Universities(No.2-9-2022-062)。

摘　　要：1 Introduction.The superior performance of deep models in classification tasks relies heavily on large-scale supervision data with rich features[1].Recent research has shown that improving the feature diversity while expanding the data scale can improve the classification performance[2,3].Time series augmentation possessing the dual strategy is essential in successfully applying deep models in time series classification.

关 键 词：CLASSIFICATION SERIES MIXING 

分 类 号：O211.61[理学—概率论与数理统计]