Stone, C. and Bull, L.
For real! XCS with continuous-valued inputs.
Evolutionary Computation, 11 (3).
Available from: http://eprints.uwe.ac.uk/5887
- Published Version
Publisher's URL: http://dx.doi.org/10.1162/106365603322365315
|Additional Information:||Since they can be used to evolve traditional production system rules, Learning Classifier Systems have proven useful tools for data mining and knowledge discovery. This paper explores the most efficient way by which to represent rules consisting of vectors of real numbers, both formally and experimentally, as this is perhaps the most typical form of real-world data sets. The paper presents a new representation which has been widely adopted by the users of these systems. As a consequence, an EPSRC project (GR/T18455/01) to create super-computer implementations of such systems and other machine learning techniques was obtained - the Super Computer Data Mining Toolkit hosted by the AI Group. This is currently being used by the Group to explore Olympic athlete data (EP/43488/01), breast cancer data for a local health trust, bowling technique for the English Cricket Board, and the system identification of complex systems considering memory (EP/E042981/01).
Copyright of this article is (c) MIT Press, 2003.|
|Uncontrolled Keywords:||XCS, continuous-valued inputs|
|Faculty/Department:||Faculty of Environment and Technology|
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|Deposited On:||22 Jan 2010 15:10|
|Last Modified:||16 Nov 2016 01:51|
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