- author = "Skylar Marvel and Alison Motsinger-Reif",
- title = "Grammatical evolution support vector machines for predicting human genetic disease association",
- booktitle = "GECCO 2012 Graduate Students Workshop",
- year = "2012",
- editor = "Alison Motsinger-Reif",
- isbn13 = "978-1-4503-1178-6",
- keywords = "genetic algorithms, genetic programming, Grammatical evolution, SVN",
- pages = "595--598",
- month = "7-11 " # jul,
- organisation = "SIGEVO",
- address = "Philadelphia, Pennsylvania, USA",
-
DOI = "
doi:10.1145/2330784.2330881",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- abstract = "Identifying genes that predict common, complex human diseases is a major goal of human genetics. This is made difficult by the effect of epistatic interactions and the need to analyze datasets with high-dimensional feature spaces. Many classification methods have been applied to this problem, one of the more recent being Support Vector Machines (SVM). Selection of which features to include in the SVM model and what parameters or kernels to use can often be a difficult task. This work uses Grammatical Evolution (GE) as a way to choose features and parameters. Initial results look promising and encourage further development and testing of this new approach.",
-
notes = "Also known as \cite{2330881} Distributed at
GECCO-2012.
ACM Order Number 910122.",
