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Using genetic programming to evolve a team of data classifiers

Lookup NU author(s): Alexander Morrison, Dr Dominic Searson, Dr Mark Willis

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Abstract

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.


Publication metadata

Author(s): Morrison GA, Searson DP, Willis MJ

Publication type: Article

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


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