Extracting comprehensible rules from neural networks via genetic algorithms

Santos, R. and Nievola, Julio C. and Freitas, Alex A. (2000) Extracting comprehensible rules from neural networks via genetic algorithms. In: Proc. 2000 IEEE Symp. on Combinations of Evolutionary Computation and Neural Networks (ECNN-2000), 05/11/2000 - 05/13/2000, San Antonio, TX, USA. (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)

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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: Conference or workshop item (Paper)
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 31 Oct 2009 11:59
Last Modified: 15 Jul 2014 14:07
Resource URI: https://kar.kent.ac.uk/id/eprint/22030 (The current URI for this page, for reference purposes)
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