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Lookup NU author(s): Alexander Morrison, Dr Dominic Searson, Dr Mark Willis
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The purpose of this paper is to demonstrate the ability of a genetic programming (GP) algorithm to evolve a team of data classification models. The GP algorithm used in this work is “multigene” in nature, i.e. there are multiple tree structures (genes) that are used to represent team members. Each team member assigns a data sample to one of a fixed set of output classes. A majority vote, determined using the mode (highest occurrence) of classes predicted by the individual genes, is used to determine the final class prediction. The algorithm is tested on a binary classification problem. For the case study investigated, compact classification models are obtained with comparable accuracy to alternative approaches.
Author(s): Morrison GA, Searson DP, Willis MJ
Publication type: Article
Publication status: Published
Journal: World Academy of Science, Engineering and Technology
Year: 2010
Issue: 72
Pages: 261-264
Print publication date: 01/12/2010
ISSN (print): 2010-376X
ISSN (electronic): 2010-3778
Publisher: WASET
URL: http://www.waset.org/journals/waset/v72/v72-51.pdf
