Biblio

The adoption of artificial intelligence (AI) into domains that impact human life (healthcare, agriculture, security and defense, etc.) has led to an increased demand for explainable AI (XAI). Herein, we focus on an under represented piece of the XAI puzzle, information fusion. To date, a number of low-level XAI explanation methods have been proposed for the fuzzy integral (FI). However, these explanations are tailored to experts and its not always clear what to do with the information they return. In this article we review and categorize existing FI work according to recent XAI nomenclature. Second, we identify a set of initial actions that a user can take in response to these low-level statistical, graphical, local, and linguistic XAI explanations. Third, we investigate the design of an interactive user friendly XAI report. Two case studies, one synthetic and one real, show the results of following recommended actions to understand and improve tasks involving classification.

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.

Fuzzy density is an important part of fuzzy integral, which is used to describe the reliability of classifiers in the process of fusion. Most of the fuzzy density assignment methods are based on the training priori knowledge of the classifier and ignore the difference of the testing samples themselves. To better describe the real-time reliability of the classifier in the fusion process, the dispersion of the classifier is calculated according to the decision information which outputted by the classifier. Then the divisibility of the classifier is obtained through the information entropy of the dispersion. Finally, the divisibility and the priori knowledge are combined to get the fuzzy density which can be dynamically adjusted. Experiments on JAFFE and CK databases show that, compared with traditional fuzzy integral methods, the proposed method can effectively improve the decision performance of fuzzy integral and reduce the interference of unreliable output information to decision. And it is an effective multi-classifier fusion method.