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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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

Ephrem Tibebe Mekonnen^1,2, Luca Longo^1,2, Pierpaolo Dondio¹

¹School of Computer Science, College of Health and Science, Technological University Dublin, Dublin, Ireland.

Frontiers in Artificial Intelligence

|October 7, 2024

Summary

This summary is machine-generated.

This study introduces a new post-hoc explainable AI method for deep learning time series classifiers. The approach generates decision tree rules to reveal key time steps, enhancing model interpretability.

Keywords:

Explainable Artificial Intelligence decision tree deep learning model agnostic post-hoc time series classification

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Area of Science:

Artificial Intelligence
Machine Learning
Data Science

Background:

Time series classification is crucial but often uses black-box deep learning models.
Existing explainable AI (XAI) methods are difficult to adapt for time series data due to its temporal nature.

Purpose of the Study:

To propose a novel global post-hoc explainable method for deep learning-based time series classifiers.
To enhance the interpretability of complex time series classification models.

Main Methods:

Training and evaluating deep learning time series classifiers.
Extracting and clustering parameterized primitive events (e.g., increasing, decreasing, local extrema) to identify prototypical events.
Using these prototypical events as input for a decision tree classifier trained on model predictions.

Main Results:

The proposed method generates decision tree graphs and rule sets as explanations.
Experiments on UCR archive datasets demonstrate improved global interpretability.
Evaluated using metrics like accuracy, fidelity, robustness, number of nodes, and rule depth.

Conclusions:

The novel global post-hoc method effectively enhances the interpretability of deep learning time series classifiers.
Decision tree rule extraction provides understandable insights into model behavior.