트랜스포머 기반 다변량 시계열 분류 모델에 대한 XAI 기법 비교 = Comparison of XAI Methods for Transformer based Multivariate Time Series Classification Model

다국어 초록 (Multilingual Abstract)

Multivariate time series classification problems are being studied in many application fields. Many AI-based models have been developed recently, and their performances are improving. Many AI models being developed require the explainability of the model output in the form of a black box. This requirement is increasing in major decision-making fields such as medicine, finance, and military, and various AI technologies (XAI) that can explain AI models have been proposed in response to this demand. In this study, we considered the trajectory of a ballistic missile as a multivariate time series data, applied XAI techniques to a transformer-based model that receives the trajectory as input and classifies the type of ballistic missile. We applied LIME, SHAP, and gradient- based analysis methods as XAI techniques suitable for the transformer-based multivariate time series classification model, and compared the analysis results of each method. By applying each method, we identified the most important variable among the various feature variables used in the model, and additionally identified which feature variable is important in which section by considering the characteristics of the time series data. We also compared the verification results for each technique. By applying various XAI techniques and comparing the results, we were able to present the explainability of the model, which will not only increase the reliability of the model’s classification results, but also contribute to improving the model’s performance in the future.