Estimating photometric redshifts using genetic algorithms

Miles, Nick and Freitas, Alex A. and Serjeant, Stephen (2006) Estimating photometric redshifts using genetic algorithms. In: Ellis, Richard and Allen, Tony and Tuson, Andrew, eds. Applications and innovations in intelligent systems XIV - Proc. of AI-2006. Springer, New York pp. 75-87. ISBN 1846286654. (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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Photometry is used as a cheap and easy way to estimate redshifts of galaxies, which would otherwise require considerable amounts of expensive telescope time. However, the analysis of photometric redshift datasets is a task where it is sometimes difficult to achieve a high classification accuracy. This work presents a custom Genetic Algorithm (GA) for mining the Hubble Deep Field North (HDF-N) datasets to achieve accurate IF-THEN classification rules. This kind of knowledge representation has the advantage of being intuitively comprehensible to the user, facilitating astronomers' interpretation of discovered knowledge. The GA is tested against the state of the art decision tree algorithm C5.0 [6] achieving significantly better results.

Item Type: Conference or workshop item (Paper)
Uncontrolled keywords: genetic algorithms, data mining, astronomy, classification rules
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: 24 Nov 2008 18:03
Last Modified: 25 Jun 2014 08:20
Resource URI: (The current URI for this page, for reference purposes)
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