Improving neural network interpretability via rule extraction

Gomez Schnyder, Stéphane (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland ; Computational Intelligence for Computational Biology (CI4CB), SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland) ; Despraz, Jérémie (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland ; Computational Intelligence for Computational Biology (CI4CB), SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland) ; Peña-Reyes, Carlos Andrés (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland ; Computational Intelligence for Computational Biology (CI4CB), SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland)

We present a method to replace the fully-connected layers of a Convolutional Neural Network (CNN9 with a small set of rules, allowing for better interpretation of its decisions while preserving accuracy.


Keywords:
Conference Type:
short paper
Faculty:
Ingénierie et Architecture
School:
HEIG-VD
Institute:
IICT - Institut des Technologies de l'Information et de la Communication
Publisher:
Rhodes, Greece, 4-7 October 2018
Date:
2018-10
Rhodes, Greece
4-7 October 2018
Pagination:
3 p.
Published in:
Proceedings Part I of Artificial Neural Networks and Machine Learning – ICANN 2018, 27th International Conference on Artificial Neural Networks, 4-7 October 2018, Rhodes, Greece
Numeration (vol. no.):
pp. 811-813
DOI:
Appears in Collection:

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 Record created 2020-01-31, last modified 2020-02-06

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