Classification of Gene Expression Data by Majority
Voting Genetic Programming Classifier
T. Paul, Y. Hasegawa, and H. Iba. Proceedings of the 2006 IEEE Congress on Evolutionary
Computation, page 2521--2528. Vancouver, BC, Canada, IEEE Computational Intelligence Society, IEEE Press, (16-21 July 2006)
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.
%0 Conference Paper
%1 Paul:CoG:cec2006
%A Paul, Topon Kumar
%A Hasegawa, Yoshihiko
%A Iba, Hitoshi
%B Proceedings of the 2006 IEEE Congress on Evolutionary
Computation
%C Vancouver, BC, Canada
%D 2006
%E Yen, Gary G.
%E Lucas, Simon M.
%E Fogel, Gary
%E Kendall, Graham
%E Salomon, Ralf
%E Zhang, Byoung-Tak
%E Coello, Carlos A. Coello
%E Runarsson, Thomas Philip
%I IEEE Press
%K algorithms, and brain breast cancer, classification, genetic majority pattern programming, recognition voting
%P 2521--2528
%T Classification of Gene Expression Data by Majority
Voting Genetic Programming Classifier
%U http://ieeexplore.ieee.org/servlet/opac?punumber=11108
%X 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.
%@ 0-7803-9487-9
@inproceedings{Paul:CoG:cec2006,
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.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Vancouver, BC, Canada},
author = {Paul, Topon Kumar and Hasegawa, Yoshihiko and Iba, Hitoshi},
biburl = {https://www.bibsonomy.org/bibtex/28c0761739fddc778430d8530564cc4cc/brazovayeye},
booktitle = {Proceedings of the 2006 IEEE Congress on Evolutionary
Computation},
editor = {Yen, Gary G. and Lucas, Simon M. and Fogel, Gary and Kendall, Graham and Salomon, Ralf and Zhang, Byoung-Tak and Coello, Carlos A. Coello and Runarsson, Thomas Philip},
interhash = {ab1fd3339ba0eeb830e3b2c7918f2347},
intrahash = {8c0761739fddc778430d8530564cc4cc},
isbn = {0-7803-9487-9},
keywords = {algorithms, and brain breast cancer, classification, genetic majority pattern programming, recognition voting},
month = {16-21 July},
notes = {WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.
IEEE Catalog Number: 06TH8846D},
organization = {IEEE Computational Intelligence Society},
pages = {2521--2528},
publisher = {IEEE Press},
timestamp = {2008-06-19T17:49:19.000+0200},
title = {Classification of Gene Expression Data by Majority
Voting Genetic Programming Classifier},
url = {http://ieeexplore.ieee.org/servlet/opac?punumber=11108},
year = 2006
}