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)

2018-10

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

pp. 811-813

Rhodes, Greece, 4-7 October 2018

3 p.

Artificial Neural Networks and Machine Learning – ICANN 2018, 27th International Conference on Artificial Neural Networks, Rhodes, Greece, 2018-10-04, 2018-10-07

convolutional neural network ; deep-learning rule extraction ; random forests ; interpretability

short paper

Ingénierie et Architecture

HEIG-VD

IICT - Institut des Technologies de l'Information et de la Communication

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