Extracting boolean and probabilistic rules from trained neural networks.
Boolean functions
Dynamic programming
Neural networks
Rule extraction
Journal
Neural networks : the official journal of the International Neural Network Society
ISSN: 1879-2782
Titre abrégé: Neural Netw
Pays: United States
ID NLM: 8805018
Informations de publication
Date de publication:
Jun 2020
Jun 2020
Historique:
received:
28
12
2018
revised:
18
01
2020
accepted:
27
03
2020
pubmed:
12
4
2020
medline:
22
9
2020
entrez:
12
4
2020
Statut:
ppublish
Résumé
This paper presents two approaches to extracting rules from a trained neural network consisting of linear threshold functions. The first one leads to an algorithm that extracts rules in the form of Boolean functions. Compared with an existing one, this algorithm outputs much more concise rules if the threshold functions correspond to 1-decision lists, majority functions, or certain combinations of these. The second one extracts probabilistic rules representing relations between some of the input variables and the output using a dynamic programming algorithm. The algorithm runs in pseudo-polynomial time if each hidden layer has a constant number of neurons. We demonstrate the effectiveness of these two approaches by computational experiments.
Identifiants
pubmed: 32278262
pii: S0893-6080(20)30118-0
doi: 10.1016/j.neunet.2020.03.024
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
300-311Informations de copyright
Copyright © 2020 Elsevier Ltd. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.