- author = "Kristopher Hoover and Rachel Marceau and Tyndall Harris and David Reif and Alison Motsinger-Reif",
- title = "A comparison of GE optimized neural networks and decision trees",
- 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",
- pages = "611--614",
- month = "7-11 " # jul,
- organisation = "SIGEVO",
- address = "Philadelphia, Pennsylvania, USA",
-
DOI = "
doi:10.1145/2330784.2330885",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- abstract = "Grammatical evolution neural networks (GENN) is a commonly used method at identifying difficult to detect gene-gene and gene-environment interactions. It has been shown to be an effective tool in the prediction of common diseases using single nucleotide polymorphisms (SNPs). However, GENN lacks interpretability because it is a black box model. Therefore, grammatical evolution of decision trees (GEDT) is being considered as an alternative, as decision trees are easily interpretable for clinicians. Previously, the most effective parameters for GEDT and GENN were found using parameter sweeps. Since GEDT is much more intuitive and easy to understand, it becomes important to compare its predictive power to that of GENN. We show that it is not as effective as GENN at detecting disease causing polymorphisms especially in more difficult to detect models, but this power trade off may be worth it for interpretability.",
-
notes = "Also known as \cite{2330885} Distributed at
GECCO-2012.
ACM Order Number 910122.",
