Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
The nature of statistical learning theory
The nature of statistical learning theory
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
A Brief Introduction to Boosting
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Genetic Programming and Evolvable Machines
Genetic Programming for Mining DNA Chip Data from Cancer Patients
Genetic Programming and Evolvable Machines
Comprehensive vertical sample-based KNN/LSVM classification for gene expression analysis
Journal of Biomedical Informatics - Special issue: Biomedical machine learning
Extraction of informative genes from microarray data
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Application of the GA/KNN method to SELDI proteomics data
Bioinformatics
Journal of Biomedical Informatics - Special issue: Clinical machine learning
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Degree prediction of malignancy in brain glioma using support vector machines
Computers in Biology and Medicine
Genetic algorithms for gene expression analysis
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Risk prediction and risk factors identification from imbalanced data with RPMBGA+
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
A two step method to identify clinical outcome relevant genes with microarray data
Journal of Biomedical Informatics
A statistical data mining approach in bacteriology for bacterial identification
International Journal of Data Analysis Techniques and Strategies
Molecular Pattern Discovery Based on Penalized Matrix Decomposition
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Gene expression classification using binary rule majority voting genetic programming classifier
International Journal of Advanced Intelligence Paradigms
EvoBIO'13 Proceedings of the 11th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Computers in Biology and Medicine
Journal of Biomedical Informatics
International Journal of Data Mining and Bioinformatics
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
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In order to get a better understanding of different types of cancers and to find the possible biomarkers for diseases, recently, many researchers are analyzing the gene expression data using various machine learning techniques. However, due to a very small number of training samples compared to the huge number of genes and class imbalance, most of these methods suffer from overfitting. In this paper, we present a majority voting genetic programming classifier (MVGPC) for the classification of microarray data. Instead of a single rule or a single set of rules, we evolve multiple rules with genetic programming (GP) and then apply those rules to test samples to determine their labels with majority voting technique. By performing experiments on four different public cancer data sets, including multiclass data sets, we have found that the test accuracies of MVGPC are better than those of other methods, including AdaBoost with GP. Moreover, some of the more frequently occurring genes in the classification rules are known to be associated with the types of cancers being studied in this paper.