Bagging for a Region Oriented Symbolic Classifier

Author

de Souza, R.M.C. ; dos Saboia, A.S.

Author_Institution

Centro de Inf., Cidade Univ., Recife

fYear

2008

fDate

10-12 Sept. 2008

Firstpage

362

Lastpage

367

Abstract

Ensemble methods like bagging combine the decisions of multiple classifiers in order to obtain more accuracy than a single classifier. This paper studies the use of bagging for a region oriented symbolic classifier. Experiments with two artificial data sets, generated according to bi-variate normal distributions have been performed in order to show the usefulness of bagging for this symbolic classifier. The prediction accuracy (error rate) of the proposed ensemble is calculated through a Monte Carlo simulation method with 100 replications.

Keywords

Monte Carlo methods; learning (artificial intelligence); pattern classification; statistical distributions; Monte Carlo simulation method; bagging method; bi-variate normal distributions; ensemble methods; region oriented symbolic classifier; supervised learning; Accuracy; Artificial neural networks; Bagging; Data analysis; Decision trees; Error analysis; Gaussian distribution; Hybrid intelligent systems; Supervised learning; Training data; classification; interval data; oriented approach; symbolic data; symbolic data analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Hybrid Intelligent Systems, 2008. HIS '08. Eighth International Conference on

Conference_Location

Barcelona

Print_ISBN

978-0-7695-3326-1

Electronic_ISBN

978-0-7695-3326-1

Type

conf

DOI

10.1109/HIS.2008.15

Filename

4626656