Prediction of cancer class with majority voting genetic programming classifier using gene expression data

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@Article{Paul:2007:TCBB,

• author = "Topon Kumar Paul and Hitoshi Iba",
• title = "Prediction of cancer class with majority voting genetic programming classifier using gene expression data",
• journal = "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
• year = "2009",
• month = apr # "-" # jun,
• volume = "6",
• number = "2",
• pages = "353--367",
• keywords = "genetic algorithms, genetic programming, Classifier design and evaluation, Data mining, Feature extraction or construction, Evolutionary computing, AdaBoost.M1, kNN, SVM, RPMBGA, EGPC Java, MVGPC",
• ISSN = "1545-5963",
• DOI = "doi:10.1109/TCBB.2007.70245",
• size = "14 pages",
• abstract = "In order to get a better understanding of different types of cancers and to find the possible biomarkers for diseases, recently many researchers are analysing the gene expression data using various machine learning techniques. However, due to smaller number of training samples compared to huge number of genes and class imbalance, most of these methods suffer from over-fitting. In this article, we present a majority voting genetic programming classifier (MVGPC) for classification of microarray data. Instead of a single rule or a single set of rules, we evolve multiple rules with genetic programming 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 genetic programming. 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 article.",
• notes = "4 genechip datasets (brain cancer prostate cancer, breast cancer, lung carcinoma) Small sample size 50, 102, 22, 203. Preprocessing reduces to 4434, 5966, 3226, 3312 genes. affymetrix software (gene present(P), missing(M), or A (unknown?) pop=4000, max rule size=100, elitism. GP classifiers combined externally by fixed rule (ie majority voting). MVGPC multiclass=multiple one vs rest. point mutation, overfitting, log transforms. {"}it may not matter whether the data are normalised or not.{"} No differece (at 5percent) between scaled and non-scaled.

  Also known as \cite{10.1109/TCBB.2007.70245} \cite{4359894}",

}

Genetic Programming entries for Topon Kumar Paul Hitoshi Iba

Citations