Contrastive Explanations for a Deep Learning Model on Time-Series Data Article Swipe

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Jokin Labaien , Ekhi Zugasti , Xabier De Carlos ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.1007/978-3-030-59065-9_19

In the last decade, with the irruption of Deep Learning \n(DL), artificial intelligence has risen a step concerning previous years. \nAlthough Deep Learning models have gained strength in many fields like \nimage classification, speech recognition, time-series anomaly detection, \netc. these models are often difficult to understand because of their lack of \ninterpretability. In recent years an effort has been made to understand \nDL models, creating a new research area called Explainable Artificial \nIntelligence (XAI). Most of the research in XAI has been done for image \ndata, and little research has been done in the time-series data field. In this \npaper, a model-agnostic method called Contrastive Explanation Method \n(CEM) is used for interpreting a DL model for time-series classification. \nEven though CEM has been validated in tabular data and image data, \nthe obtained experimental results show that CEM is also suitable for \ninterpreting deep learning models that work with time-series data.

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Computer Science

Artificial Intelligence

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Interpretability Computer science Artificial intelligence Deep learning Field (mathematics) Series (stratigraphy) Time series Machine learning Anomaly detection Pattern recognition (psychology) Mathematics Paleontology Biology Pure mathematics

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Type: book-chapter
Language: en
Landing Page: https://doi.org/10.1007/978-3-030-59065-9_19
OA Status: gold
Cited By: 10
References: 16
Related Works: 10
OpenAlex ID: https://openalex.org/W3085010542

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OpenAlex ID: https://openalex.org/W3085010542

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DOI: https://doi.org/10.1007/978-3-030-59065-9_19

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Title: Contrastive Explanations for a Deep Learning Model on Time-Series Data

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Type: book-chapter

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Language: en

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Publication year: 2020

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Publication date: 2020-01-01

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Authors: Jokin Labaien, Ekhi Zugasti, Xabier De Carlos

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Landing page: https://doi.org/10.1007/978-3-030-59065-9_19

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OA URL: https://hdl.handle.net/10810/70493

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Concepts: Interpretability, Computer science, Artificial intelligence, Deep learning, Field (mathematics), Series (stratigraphy), Time series, Machine learning, Anomaly detection, Pattern recognition (psychology), Mathematics, Paleontology, Biology, Pure mathematics

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Cited by: 10

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sustainable_development_goals[0].id	https://metadata.un.org/sdg/4
sustainable_development_goals[0].score	0.5099999904632568
sustainable_development_goals[0].display_name	Quality Education
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