Title :
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
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;
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
DOI :
10.1109/HIS.2008.15
