Explaining Deep Learning Time Series Classification Models using a Decision Tree

Authors

Ephrem T. Mekonnen, Technological University DublinFollow
Pierpaolo Dondio, Technological University DublinFollow
Luca Longo, Technological University DublinFollow

Files

Publisher

Technological University Dublin

Description

This preliminary study proposes a new post hoc method to explain deep learning-based time series classification models using a decision tree. Our approach generates a decision tree graph or rulesets as an explanation, improving interpretability compared to saliency map-based methods. The method involves two phases: training and evaluating the deep learning-based time series classification model and extracting prototypical events from the evaluation set to train the decision tree classifier. We conducted experiments on artificial and real datasets, evaluating the explanations based on accuracy, fidelity, number of nodes, and depth. Our preliminary findings suggest that our post-hoc method improves the interpretability and trust of complex time series classification models.

Publication Date

2023

Keywords

time series classification models, deep learning, decision tree

Disciplines

Computer Sciences

Conference

XAI conference, Lisbon, Portugal

DOI

https://doi.org/10.21427/9YKT-WZ47

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.

Recommended Citation

Mekonnen, E. T., Dondio, P., & Longo, L. (2023). Explaining Deep Learning Time Series Classification Models using a Decision Tree. Technological University Dublin. DOI: 10.21427/9YKT-WZ47