title = "GP Ensembles for improving multi-class prediction
problems",
booktitle = "AI*IA Workshop on Evolutionary Computation,
Evoluzionistico GSICE05",
year = "2005",
editor = "Sara Manzoni and Matteo Palmonari and Fabio Sartori",
address = "University of Milan Bicocca, Italy",
month = "20 " # sep,
keywords = "genetic algorithms, genetic programming, data mining,
classification, boosting",
ISBN = "88-900910-0-2",
size = "10 pages",
abstract = "Cellular Genetic Programming for data classification
extended with the boosting technique to induce an
ensemble of predictors is presented. The method
implements in parallel AdaBoost.M2 to efficiently deal
with multi-class problems and it is able to manage
large data sets that do not fit in main memory since
each classifier is trained on a subset of the overall
training data. Experiments on several data sets show
that, by using a training set of reduced size, better
classification accuracy can be obtained at a much lower
computational cost.",
notes = "http://www.ce.unipr.it/people/cagnoni/gsice2005/gsice-eng.pdf
http://www.ce.unipr.it/people/cagnoni/gsice2005/
Workshop proceedings on CD-ROM only. Workshop held
in-conjunction with the IX Congress of the Italian
Association for Artificial Intelligence. In
English.
ICAR-CNR, Via P.Bucci 41C, Univ. della Calabria 87036
Rende (CS), Italy
