A global model-agnostic rule-based XAI method based on Parameterized Event Primitives for time series classifiers.

ABSTRACT: Time series classification is a challenging research area where machine learning and deep learning techniques have shown remarkable performance. However, often, these are seen as black boxes due to their minimal interpretability. On the one hand, there is a plethora of eXplainable AI (XAI) methods designed to elucidate the functioning of models trained on image and tabular data. On the other hand, adapting these methods to explain deep learning-based time series classifiers may not be straightforward due to the temporal nature of time series data. This research proposes a novel global post-hoc explainable method for unearthing the key time steps behind the inferences made by deep learning-based time series classifiers. This novel approach generates a decision tree graph, a specific

SUBMITTER: Mekonnen ET 

PROVIDER: S-EPMC11449859 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature