Presentation 1998/6/18
Rule Extraction from Neural Networks Formed Using Evolutionary Algorithms
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Abstract(in English) This paper presents a method of extracting rules from neural networks trained using a random optimization method(ROM) with deterministic mutation(DM). The DM is performed on the basis of the result of neural network structure learning. The ROM with the DM is utilized to reduce the number of network connections for iris data. The network connections survived after training represent rules to perform pattern classification for the iris data. The rules are then extracted from the neural network in which hidden units use signum output functions to produce binary values. It enables us to extract simple logical functions from the network. Simulation results for the iris data show this method can generate simple rules compared with conventional methods.
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Keyword(in English) evolutionary algorithm / rule extraction / deterministic mutation / structure learning
Paper # NC98-22,HIP98-13
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Conference Information
Committee NC
Conference Date 1998/6/18(1days)
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Paper Information
Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Rule Extraction from Neural Networks Formed Using Evolutionary Algorithms
Sub Title (in English)
Keyword(1) evolutionary algorithm
Keyword(2) rule extraction
Keyword(3) deterministic mutation
Keyword(4) structure learning
1st Author's Name Minoru FUKUMI
1st Author's Affiliation University of TOKUSHIMA()
2nd Author's Name Norio AKAMATSU
2nd Author's Affiliation University of TOKUSHIMA
Date 1998/6/18
Paper # NC98-22,HIP98-13
Volume (vol) vol.98
Number (no) 128
Page pp.pp.-
#Pages 8
Date of Issue