%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 }