Explainable AI for understanding decisions and data-driven optimization of the Choquet integral
Document Type
Conference Proceeding
Publication Date
10-12-2018
Abstract
© 2018 IEEE. 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.
Publication Title
IEEE International Conference on Fuzzy Systems
Recommended Citation
Murray, B.,
Aminul Islam, M.,
Pinar, A.,
Havens, T.,
Anderson, D.,
&
Scott, G.
(2018).
Explainable AI for understanding decisions and data-driven optimization of the Choquet integral.
IEEE International Conference on Fuzzy Systems,
2018-July.
http://doi.org/10.1109/FUZZ-IEEE.2018.8491501
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/10489