Gutenberg Open Science: Rule extraction from binary neural networks with convolutional rules for model validation

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-6523

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DC Field	Value	Language
dc.contributor.author	Burkhardt, Sophie	-
dc.contributor.author	Brugger, Jannis	-
dc.contributor.author	Wagner, Nicole	-
dc.contributor.author	Ahmadi, Zahra	-
dc.contributor.author	Kersting, Kristian	-
dc.contributor.author	Kramer, Stefan	-
dc.date.accessioned	2021-11-16T10:04:59Z	-
dc.date.available	2021-11-16T10:04:59Z	-
dc.date.issued	2021	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/6533	-
dc.description.abstract	Classification approaches that allow to extract logical rules such as decision trees are often considered to be more interpretable than neural networks. Also, logical rules are comparatively easy to verify with any possible input. This is an important part in systems that aim to ensure correct operation of a given model. However, for high-dimensional input data such as images, the individual symbols, i.e. pixels, are not easily interpretable. Therefore, rule-based approaches are not typically used for this kind of high-dimensional data. We introduce the concept of first-order convolutional rules, which are logical rules that can be extracted using a convolutional neural network (CNN), and whose complexity depends on the size of the convolutional filter and not on the dimensionality of the input. Our approach is based on rule extraction from binary neural networks with stochastic local search. We show how to extract rules that are not necessarily short, but characteristic of the input, and easy to visualize. Our experiments show that the proposed approach is able to model the functionality of the neural network while at the same time producing interpretable logical rules. Thus, we demonstrate the potential of rule-based approaches for images which allows to combine advantages of neural networks and rule learning.	en_GB
dc.description.sponsorship	Open Access-Publizieren Universität Mainz / Universitätsmedizin Mainz	de
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	004 Informatik	de_DE
dc.subject.ddc	004 Data processing	en_GB
dc.title	Rule extraction from binary neural networks with convolutional rules for model validation	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-6523	-
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 08 Physik, Mathematik u. Informatik	de
jgu.organisation.number	7940	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Frontiers in artificial intelligence	de
jgu.journal.volume	4	de
jgu.pages.alternative	642263	de
jgu.publisher.year	2021	-
jgu.publisher.name	Frontiers Media	de
jgu.publisher.place	Lausanne	de
jgu.publisher.uri	https://doi.org/10.3389/frai.2021.642263	de
jgu.publisher.issn	2624-8212	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	004	de
jgu.publisher.doi	10.3389/frai.2021.642263
jgu.organisation.ror	https://ror.org/023b0x485
Appears in collections:	JGU-Publikationen

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