Explaining Deep Learning Time Series Classification Models using a Decision Tree-Based Post-Hoc XAI Method

Ephrem T. Mekonnen, Pierpaolo Dondio, Luca Longo

Research output: Contribution to journalConference articlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)71-76
Number of pages6
JournalCEUR Workshop Proceedings
Volume3554
Publication statusPublished - 2023
EventJoint 1st World Conference on eXplainable Artificial Intelligence: Late-Breaking Work, Demos and Doctoral Consortium, xAI-2023: LB-D-DC - Lisbon, Portugal
Duration: 26 Jul 202328 Jul 2023

Keywords

  • Decision Tree
  • Deep Learning
  • Explainable Artificial Intelligence
  • Time Series Classification

Fingerprint

Dive into the research topics of 'Explaining Deep Learning Time Series Classification Models using a Decision Tree-Based Post-Hoc XAI Method'. Together they form a unique fingerprint.

Cite this