Using genetic programming to evolve a team of data classifiers

  1. Lookup NU author(s)
  2. Alexander Morrison
  3. Dr Dominic Searson
  4. Dr Mark Willis
Author(s)Morrison GA, Searson DP, Willis MJ
Publication type Article
JournalWorld Academy of Science, Engineering and Technology
Year2010
Volume
Issue72
Pages261-264
ISSN (print)2010-376X
ISSN (electronic)2010-3778
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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.
PublisherWASET
URLhttp://www.waset.org/journals/waset/v72/v72-51.pdf
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