- author = "Topon Kumar Paul and Yoshihiko Hasegawa and Hitoshi Iba",
- title = "Classification of Gene Expression Data by Majority Voting Genetic Programming Classifier",
- booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation",
- year = "2006",
- 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",
- pages = "2521--2528",
- address = "Vancouver, BC, Canada",
- month = "16-21 " # jul,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, pattern recognition and classification, breast cancer, brain cancer, majority voting",
- ISBN = "0-7803-9487-9",
-
URL = "
http://www.iba.k.u-tokyo.ac.jp/~topon/Papers/CEC2006.pdf",
-
URL = "
http://ieeexplore.ieee.org/servlet/opac?punumber=11108",
-
DOI = "
doi:10.1109/CEC.2006.1688622",
- 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.",
-
notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.
IEEE Catalog Number: 06TH8846D",
