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- W2896046721 abstract "To date, numerous ways have been created to learn a fusion solution from data. However, a gap exists in terms of understanding the quality of what was learned and how trustworthy the fusion is for future—i.e., new—data. In part, the current paper is driven by the demand for so-called explainable AI (XAI). Herein, we discuss methods for XAI of the Choquet integral (ChI), a parametric nonlinear aggregation function. Specifically, we review existing indices, and we introduce new data-centric XAI tools. These various XAI-ChI methods are explored in the context of fusing a set of heterogeneous deep convolutional neural networks for remote sensing." @default.
- W2896046721 created "2018-10-26" @default.
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- W2896046721 date "2018-07-01" @default.
- W2896046721 modified "2023-09-28" @default.
- W2896046721 title "Explainable AI for Understanding Decisions and Data-Driven Optimization of the Choquet Integral" @default.
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- W2896046721 doi "https://doi.org/10.1109/fuzz-ieee.2018.8491501" @default.
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