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Extracting comprehensible rules from neural networks via genetic algorithms

Santos, R.T. and Nievola, Julio C. and Freitas, Alex A. (2000) Extracting comprehensible rules from neural networks via genetic algorithms. In: 2000 IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks. Proceedings of the First IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks. IEEE, pp. 130-139. ISBN 0-7803-6572-0. (doi:10.1109/ECNN.2000.886228) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:22030)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1109/ECNN.2000.886228

Abstract

A common problem in KDD (Knowledge Discovery in Databases) is the presence of noise in the data being mined. Neural networks are robust and have a good tolerance to noise, which makes them suitable for mining very noisy data. However, they have the well-known disadvantage of not discovering any high-level rule that can be used as a support for human decision making. In this work we present a method for extracting accurate, comprehensible rules from neural networks. The proposed method uses a genetic algorithm to find a good neural network topology. This topology is then passed to a rule extraction algorithm, and the quality of the extracted rules is then fed back to the genetic algorithm. The proposed system is evaluated on three public-domain data sets and the results show that the approach is valid

Item Type: Book section
DOI/Identification number: 10.1109/ECNN.2000.886228
Uncontrolled keywords: neural networks; genetic algorithms; data mining; network topology; humans; algorithm design and analysis; databases; noise robustness; decision making; back
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 31 Oct 2009 11:59 UTC
Last Modified: 16 Feb 2021 12:32 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/22030 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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