Biblio

Filters: Author is Pinar, A. J.  [Clear All Filters]
2018-12-10
Murray, B., Islam, M. A., Pinar, A. J., Havens, T. C., Anderson, D. T., Scott, G..  2018.  Explainable AI for Understanding Decisions and Data-Driven Optimization of the Choquet Integral. 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1–8.

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.