Murray, Bryce
(Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, USA)
,
Islam, M. Aminul
(Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, USA)
,
Pinar, Anthony J.
(Electrical and Computer Engineering, Michigan Technological University, Houghton, MI, USA)
,
Havens, Timothy C.
(Electrical and Computer Engineering, Michigan Technological University, Houghton, MI, USA)
,
Anderson, Derek T.
(Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA)
,
Scott, Grant
(Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA)
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 e...
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.
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.
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