Titlebook: Interpretable Artificial Intelligence: A Perspective of Granular Computing; Witold Pedrycz,Shyi-Ming Chen Book 2021 The Editor(s) (if appl

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Visualizing the Behavior of Convolutional Neural Networks for Time Series Forecasting,isualization?techniques to make them more interpretable. In this chapter, we adapt image visualization?algorithms to time series problems, allowing us to build granular, intuitively interpretable feature hierarchies to make a time series forecast as understandable as an image recognition task. We do

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Beyond Deep Event Prediction: Deep Event Understanding Based on Explainable Artificial Intelligencecial Intelligence (XAI) driven by the paradigm of Granular Computing (GrC). It semantically models knowledge by imitating human thinking which can be understood by decision-makers. DUE offers a level of understanding that is both argumentative and explainable for critical applications and systems. D

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Factual and Counterfactual Explanation of Fuzzy Information Granules,y the rest of classes are not selected. Thus, endowing FURIA rules with the capability to generate a combination of both factual and counterfactual explanations is likely to make them more trustworthy. We illustrate how to build self-explaining FURIA classifiers in two practical use cases regarding

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Survey of Explainable Machine Learning with Visual and Granular Methods Beyond Quasi-Explanations,g research focus. While multiple efficient methods for visual representation of high-dimensional data exist, the loss of interpretable information, occlusion, and clutter continue to be a challenge, which lead to quasi-explanations. This chapter starts with the motivation and the definitions of diff

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