Result: Ensemble techniques for parallel Genetic Programming based classifiers
Title:
Ensemble techniques for parallel Genetic Programming based classifiers
Source:
Genetic programming (Essex, 14-16 April 2003)Lecture notes in computer science. :59-69
Publisher Information:
Berlin: Springer, 2003.
Publication Year:
2003
Physical Description:
print, 23 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Systèmes d'information. Bases de données, Information systems. Data bases, Algorithme génétique, Genetic algorithm, Algoritmo genético, Classificateur, Classifier, Clasificador, Classification, Clasificación, Programmation parallèle, Parallel programming, Programación paralela, Classification donnée, Programmation génétique cellulaire, Programmation génétique, Genetic programming
Document Type:
Conference
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
ICAR-CNR, c/o DEIS, Univ. della Calabria, 87036 Rende (CS), Italy
ISSN:
0302-9743
Rights:
Copyright 2003 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.14934037
Database:
PASCAL Archive
Further Information
An extension of Cellular Genetic Programming for data classification to induce an ensemble of predictors is presented. Each classifier is trained on a different subset of the overall data, then they are combined to classify new tuples by applying a simple majority voting algorithm, like bagging. Preliminary results on a large data set show that the ensemble of classifiers trained on a sample of the data obtains higher accuracy than a single classifier that uses the entire data set at a much lower computational cost.