A STUDY ON RULE EXTRACTION FROM SEVERAL COMBINED NEURAL NETWORKS
2001 ◽
Vol 11
(03)
◽
pp. 247-255
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Keyword(s):
Np Hard
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The problem of rule extraction from neural networks is NP-hard. This work presents a new technique to extract "if-then-else" rules from ensembles of DIMLP neural networks. Rules are extracted in polynomial time with respect to the dimensionality of the problem, the number of examples, and the size of the resulting network. Further, the degree of matching between extracted rules and neural network responses is 100%. Ensembles of DIMLP networks were trained on four data sets in the public domain. Extracted rules were on average significantly more accurate than those extracted from C4.5 decision trees.
2019 ◽
Vol 64
(6)
◽
pp. 669-675
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2021 ◽
Vol 5
(9 (113))
◽
pp. 82-90
Keyword(s):
2009 ◽
Vol 60
(12)
◽
pp. 3051-3059
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2017 ◽
Vol 43
(4)
◽
pp. 26-32
◽
2000 ◽
Vol 10
(04)
◽
pp. 267-279
◽
Keyword(s):
2012 ◽
Vol 22
(01)
◽
pp. 77-87
◽
2009 ◽
Vol 19
(02)
◽
pp. 67-89
◽