PIP: Pictorial Interpretable Prototype Learning for Time Series Classification
IEEE Computational Intelligence Magazine(2022)
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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