@inproceedings{Paul:CoG:cec2006,
	title = {Classification of Gene Expression Data by Majority
                 Voting Genetic Programming Classifier},
	address = {Vancouver, BC, Canada},
	author = {Topon Kumar Paul and Yoshihiko Hasegawa and Hitoshi Iba},
	booktitle = {Proceedings of the 2006 IEEE Congress on Evolutionary
                 Computation},
	editor = {Gary G. Yen and Simon M. Lucas and Gary Fogel and Graham Kendall and Ralf Salomon and Byoung-Tak Zhang and Carlos A. Coello Coello and Thomas Philip Runarsson},
	month = {16-21 July},
	organization = {IEEE Computational Intelligence Society},
	pages = {2521--2528},
	publisher = {IEEE Press},
	url = {http://ieeexplore.ieee.org/servlet/opac?punumber=11108},
	year = {2006},
	biburl = {http://www.bibsonomy.org/bibtex/28c0761739fddc778430d8530564cc4cc/brazovayeye},
	abstract = {Recently, genetic programming (GP) has been applied to
                 the classification of gene expression data. In its
                 typical implementation, using training data, a single
                 rule or a single set of rules is evolved with GP, and
                 then it is applied to test data to get generalised test
                 accuracy. However, in most cases, the generalized test
                 accuracy is not higher. In this paper, we propose a
                 majority voting technique for prediction of the labels
                 of test samples. Instead of a single rule or a single
                 set of rules, we evolve multiple rules with GP and then
                 apply those rules to test samples to determine their
                 labels by using the majority voting technique. We
                 demonstrate the effectiveness of our proposed method by
                 performing different types of experiments on two
                 microarray data sets.},
	isbn = {0-7803-9487-9}, notes = {WCCI 2006 - A joint meeting of the IEEE, the EPS, and
                 the IEE.

                 IEEE Catalog Number: 06TH8846D},
	keywords = {algorithms, and brain breast cancer, classification, genetic majority pattern programming, recognition voting }
}