@conference {1263,
	title = {Investigating Neighborhood Generation Methods for Explanations of Obscure Image Classifiers},
	booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining},
	year = {2019},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Given the wide use of machine learning approaches based on opaque prediction models, understanding the reasons behind decisions of black box decision systems is nowadays a crucial topic. We address the problem of providing meaningful explanations in the widely-applied image classification tasks. In particular, we explore the impact of changing the neighborhood generation function for a local interpretable model-agnostic explanator by proposing four different variants. All the proposed methods are based on a grid-based segmentation of the images, but each of them proposes a different strategy for generating the neighborhood of the image for which an explanation is required. A deep experimentation shows both improvements and weakness of each proposed approach.},
	doi = {10.1007/978-3-030-16148-4_5},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-16148-4_5},
	author = {Riccardo Guidotti and Anna Monreale and Cariaggi, Leonardo}
}
