Abstract
Biological sequence analysis presents interesting challenges for machine learning. With an important problem — the recognition of functional target sites for microRNA molecules — as an example, we show how multiple genetic programming classifiers improve accuracy and stability. Moving from single classifiers to bagging and boosting with crossvalidation and parameter optimization requires more computing power. A special-purpose search processor for fitness evaluation renders boosted genetic programming practical for our purposes.
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Saetrom, P., Birkeland, O.R., Snøve, O. (2007). Boosting Improves Stability and Accuracy of Genetic Programming in Biological Sequence Classification. In: Riolo, R., Soule, T., Worzel, B. (eds) Genetic Programming Theory and Practice IV. Genetic and Evolutionary Computation. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-49650-4_5
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DOI: https://doi.org/10.1007/978-0-387-49650-4_5
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