- title = "Ensemble {Bayesian} Model Averaging in Genetic Programming",
- author = "Alexandros Agapitos and Michael O'Neill and Anthony Brabazon",
- pages = "2451--2458",
- booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation",
- year = "2014",
- month = "6-11 " # jul,
- editor = "Carlos A. {Coello Coello}",
- address = "Beijing, China",
- ISBN = "0-7803-8515-2",
- keywords = "genetic algorithms, Genetic programming, Data mining, Classification, clustering and data analysis",
-
DOI = "
doi:10.1109/CEC.2014.6900567",
-
abstract = "This paper considers the general problem of function
estimation via Genetic Programming (GP). Data analysts
typically select a model from a population of models,
and then proceed as if the selected model had generated
the data. This approach ignores the uncertainty in
model selection, leading to over-confident inferences
and lack of generalisation.
We adopt a coherent method for accounting for this uncertainty through a weighted averaging of all models competing in a population of GP. It is a principled statistical method for post-processing a population of programs into an ensemble, which is based on Bayesian Model Averaging (BMA).
Under two different formulations of BMA, the predictive probability density function (PDF) of a response variable is a weighted average of PDFs centred around the individual predictions of component models that take the form of either standalone programs or ensembles of programs. The weights are equal to the posterior probabilities of the models generating the predictions, and reflect the models' skill on the training dataset.
The method was applied to a number of synthetic symbolic regression problems, and results demonstrate that it generalises better than standard methods for model selection, as well as methods for ensemble construction in GP.",
- notes = "WCCI2014",
