- author = "Pavel Kordik and Jan Cerny",
- booktitle = "IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL 2014)",
- title = "Building predictive models in two stages with meta-learning templates optimized by genetic programming",
- year = "2014",
- month = dec,
- keywords = "genetic algorithms, genetic programming, Meta-learning, Hierarchical ensembles, Ensemble classifiers, Bagging, Boosting, Stacking, Cascade generalisation, Non-stationary environments, Transfer learning",
-
DOI = "
doi:10.1109/CIEL.2014.7015740",
- size = "8 pages",
-
abstract = "The model selection stage is one of the most difficult
in predictive modelling. To select a model with a
highest generalisation performance involves
benchmarking huge number of candidate models or
algorithms. Often, a final model is selected without
considering potentially high quality candidates just
because there are too many possibilities. Improper
benchmarking methodology often leads to biased
estimates of model generalisation performance.
Automation of the model selection stage is possible,
however the computational complexity is huge especially
when ensembles of models and optimisation of input
features should be also considered.
In this paper we show, how to automate model selection process in a way that allows to search for complex hierarchies of ensemble models while maintaining computational tractability. We introduce two-stage learning, meta-learning templates optimised by evolutionary programming with anytime properties to be able to deliver and maintain data-tailored algorithms and models in a reasonable time without human interaction.
Co-evolution if inputs together with optimisation of templates enabled to solve algorithm selection problem efficiently for variety of datasets.",
- notes = "Also known as \cite{7015740}",
