EXplainable AI (XAI) offers a wide range of algorithmic solutions to the problem of AI's opacity, but ensuring of their usefulness remains a challenge. In this study, we propose an multi-explanation XAI system using surrogate rules, LIME and nearest neighbor on a random forest. Through an experiment in an e-sports prediction task, we demonstrate the feasibility and measure the usefulness of working with multiple forms of explanation. Considering users' preferences, we offer new perspectives for XAI design and evaluation, highlighting the concept of data difficulty and of the idea of prior agreement between users and AI.

The usual strategy for computing a distance between two distributions consists of modeling the distributions in feature space, and of computing the distance between the models. We propose here to model the distributions of points by using unsupervised trees. Our main contribution is the definition of a tree-based approximation of the Kullback-Leibler divergence for very large feature spaces, from which we derive a symmetric distance. Our tree-based KL divergence consists first of building for each set of samples a balanced tree. Then, for any pair of sets of samples, we effectively compute the KL divergence between the empirical distributions at the leaves for the set used to build the tree, and the empirical distribution at the leaves for the other set. We show experimentally on synthetic data the consistency between this quantity and the exact KL divergence, and demonstrate its efficiency for both unsupervised and supervised classification on multiple standard real-world data-sets. Our main application is the characterization of abnormal neuron development.

In this letter, we address the characterization of objects in 3-D sonar images of the water column obtained by a multibeam echo sounder. Compared with classic 2-D images from a monobeam echo sounder, these 3-D images provide finer scale observation of the pelagic biomasses and new tools to characterize 3-D distributions. By viewing object patterns as realizations of spatial point processes, we investigate descriptive spatial statistics. This method is then applied to 3-D fisheries acoustics data set for characterization of the distribution of pelagic fish schools. Reported experiments illustrate the relevance of the proposed descriptors. The comparison of our method with 2-D sonar data analysis further demonstrates the information gain from using 3-D sonar imagery.

This paper addresses the training of classification trees for weakly labelled data. We call “weakly labelled data”, a training set such as the prior labelling information provided refers to vector that indicates the probabilities for instances to belong to each class. Classification tree typically deals with hard labelled data, in this paper a new procedure is suggested in order to train a tree from weakly labelled data. Resulting tree is different than usual in the sense that weak labels are taking into account and affected to test instances. Considering a forest, we show how trees can be associated in the test step. The proposed method is compared with typical models such as generative and discriminative methods for object recognition and we show that our model can outperform the two previous. The considered models are evaluated on standard datasets from UCI and an application to fisheries acoustics is considered.

This paper addresses weakly supervised object recognition. We show how the combination of an image-level inference, in terms of image-level object class priors, can lead to better training of object recognition models. Stated within a probabilistic setting, the proposed approach is applied to fisheries acoustics and fish school recognition.

This paper addresses the inference of probabilistic classification models using weakly supervised learning. In contrast to previous work, the use of proportion-based training data is investigated in combination to non-linear classification models. An application to fisheries acoustics and fish school classification is considered and experiments are reported for synthetic and real datasets.