Investigating Neighborhood Generation Methods for Explanations of Obscure Image Classifiers
| Title | Investigating Neighborhood Generation Methods for Explanations of Obscure Image Classifiers |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Guidotti, R, Monreale, A, Cariaggi, L |
| Conference Name | Pacific-Asia Conference on Knowledge Discovery and Data Mining |
| Publisher | 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. |
| URL | https://link.springer.com/chapter/10.1007/978-3-030-16148-4_5 |
| DOI | 10.1007/978-3-030-16148-4_5 |
| Attachment | Size |
|---|---|
 pakdd2019investigating.pdf | 3.21 MB |
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