PIP: Pictorial Interpretable Prototype Learning for Time Series Classification

IEEE Computational Intelligence Magazine(2022)

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摘要
Time series classifiers are not only challenging to design, but they are also notoriously difficult to deploy for critical applications because end users may not understand or trust black-box models. Despite new efforts, explanations generated by other interpretable time series models are complicated for non-engineers to understand. The goal of PIP is to provide time series explanations that are t...
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关键词
Interpretability,Time Series Classification,Trustworthy Machine Learning
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