WASET
	@article{(Open Science Index):https://publications.waset.org/pdf/2448,
	  title     = {Using Genetic Programming to Evolve a Team of Data Classifiers},
	  author    = {Gregor A. Morrison and  Dominic P. Searson and  Mark J. Willis},
	  country	= {},
	  institution	= {},
	  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.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {4},
	  number    = {12},
	  year      = {2010},
	  pages     = {1815 - 1818},
	  ee        = {https://publications.waset.org/pdf/2448},
	  url   	= {https://publications.waset.org/vol/48},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 48, 2010},
	}