Advancing explainability in multivariate time series classification through generative models

Advancing explainability in multivariate time series classification through generative models

As Artificial Intelligence (AI) techniques become increasingly integrated in various domains, concerns about their interpretability have increased. This has catalysed the development of eXplainable AI (XAI), which seeks to make AI systems more understandable to humans. However, most XAI research has...

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Bibliographic Details
Main Author: Meng, Han
Format: Thesis (University of Nottingham only)
Language:English
Published: 2024
Subjects:
Human-computer interaction; Artificial intelligence; Time series data; Model-agnostic framework; Counterfactual explanations; Out-of-distribution problem; XAI
Online Access:https://eprints.nottingham.ac.uk/78149/

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