Latest revision as of 10:03, 8 February 2021

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.

Original document

The different versions of the original document can be found in:

• http://dx.doi.org/10.1007/978-3-030-16148-4_5 under the license http://www.springer.com/tdm

• http://hdl.handle.net/11568/1023226

• https://publications.cnr.it/doc/417417

• http://link.springer.com/content/pdf/10.1007/978-3-030-16148-4_5,

http://dx.doi.org/10.1007/978-3-030-16148-4_5 under the license http://www.springer.com/tdm

• https://dblp.uni-trier.de/db/conf/pakdd/pakdd2019-1.html#GuidottiMC19,

https://link.springer.com/chapter/10.1007/978-3-030-16148-4_5,

https://rd.springer.com/chapter/10.1007/978-3-030-16148-4_5,

https://academic.microsoft.com/#/detail/2934229219