Weakly supervised learning with decision trees applied to fisheries acoustics.

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.