Boosting improves stability and accuracy of genetic programming in biological classification

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InCollection{Saetrom:2006:GPTP,

• author = "Pal Saetrom and Olaf Rene Birkeland and Ola {Snove Jr.}",
• title = "Boosting improves stability and accuracy of genetic programming in biological classification",
• booktitle = "Genetic Programming Theory and Practice {IV}",
• year = "2006",
• editor = "Rick L. Riolo and Terence Soule and Bill Worzel",
• volume = "5",
• series = "Genetic and Evolutionary Computation",
• pages = "61--78",
• address = "Ann Arbor",
• month = "11-13 " # may,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, Bioinformatics, microRNA, gene prediction, RNAi",
• ISBN = "0-387-33375-4",
• DOI = "doi:10.1007/978-0-387-49650-4_5",
• size = "16 pages",
• abstract = "Biological sequence analysis presents interesting challenges for machine learning. Using one of the most important current problems -- the recognition of functional target sites for microRNA molecules -- as an example, we show how joining multiple genetic programming classifiers improves accuracy and stability tremendously. When moving from single classifiers to bagging and boosting with cross validation and parameter optimisation, you require more computing power. We use a special-purpose search processor for fitness evaluation, which renders boosted genetic programming practical for our purposes.",
• notes = "part of \cite{Riolo:2006:GPTP} Published Jan 2007 after the workshop",

}

Genetic Programming entries for Pal Saetrom Olaf Rene Birkeland Ola Snove Jr

Citations