GP Ensembles for improving multi-class prediction
problems
G. Folino, C. Pizzuti, und G. Spezzano. AI*IA Workshop on Evolutionary Computation,
Evoluzionistico GSICE05, University of Milan Bicocca, Italy, (20 September 2005)
Zusammenfassung
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.
AI*IA Workshop on Evolutionary Computation,
Evoluzionistico GSICE05
Jahr
2005
Monat
20 September
isbn
88-900910-0-2
notes
http://www.ce.unipr.it/people/cagnoni/gsice2005/gsice-eng.pdf
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
%0 Conference Paper
%1 folino:2005:gsice
%A Folino, Gianluigi
%A Pizzuti, Clara
%A Spezzano, Giandomenico
%B AI*IA Workshop on Evolutionary Computation,
Evoluzionistico GSICE05
%C University of Milan Bicocca, Italy
%D 2005
%E Manzoni, Sara
%E Palmonari, Matteo
%E Sartori, Fabio
%K algorithms, boosting classification, data genetic mining, programming,
%T GP Ensembles for improving multi-class prediction
problems
%X 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.
%@ 88-900910-0-2
@inproceedings{folino:2005:gsice,
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.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {University of Milan Bicocca, Italy},
author = {Folino, Gianluigi and Pizzuti, Clara and Spezzano, Giandomenico},
biburl = {https://www.bibsonomy.org/bibtex/20676a0df1d4f886daf8cc46912c11163/brazovayeye},
booktitle = {AI*IA Workshop on Evolutionary Computation,
Evoluzionistico GSICE05},
editor = {Manzoni, Sara and Palmonari, Matteo and Sartori, Fabio},
interhash = {db99be6c6bee86491112370e859717cb},
intrahash = {0676a0df1d4f886daf8cc46912c11163},
isbn = {88-900910-0-2},
keywords = {algorithms, boosting classification, data genetic mining, programming,},
month = {20 September},
notes = {http://www.ce.unipr.it/people/cagnoni/gsice2005/gsice-eng.pdf
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},
size = {10 pages},
timestamp = {2008-06-19T17:39:42.000+0200},
title = {{GP} Ensembles for improving multi-class prediction
problems},
year = 2005
}