DocumentCode :
2650024
Title :
Homogeneous Ensemble Selection through Hierarchical Clustering with a Modified Artificial Fish Swarm Algorithm
Author :
de Oliveira, Jose F. L. ; Ludermir, Teresa B.
Author_Institution :
Center of Inf., Fed. Univ. of Pernambuco, Recife, Brazil
fYear :
2011
fDate :
7-9 Nov. 2011
Firstpage :
177
Lastpage :
180
Abstract :
In the pattern recognition field, ensembles of classifiers have been proposed as a method to overcome the natural limitations of single classifiers, and to increase the accuracy of the system. Previous studies show that ensembles of classifiers need to have accurate classifiers that have different knowledge for the same problem. In this paper, we propose an ensemble selection technique for single layer neural networks trained by the Extreme Learning Machine algorithm based on the Artificial Fish Swarm Algorithm. The ensembles are grouped based on information on the fish population using a hierarchical cluster algorithm. Experimental results show that the proposed method achieve better generalization performance than best model produced by the modified optimization technique presented in real benchmark datasets.
Keywords :
learning (artificial intelligence); neural nets; particle swarm optimisation; pattern classification; pattern clustering; classifier ensemble; ensemble selection technique; extreme learning machine algorithm; fish population information; hierarchical cluster algorithm; hierarchical clustering; modified artificial fish swarm algorithm; modified optimization technique; pattern recognition field; real benchmark dataset; single layer neural network; Accuracy; Clustering algorithms; Ionosphere; Machine learning; Neural networks; Optimization; Sonar; Ensembles; Neural Networks; Optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Tools with Artificial Intelligence (ICTAI), 2011 23rd IEEE International Conference on
Conference_Location :
Boca Raton, FL
ISSN :
1082-3409
Print_ISBN :
978-1-4577-2068-0
Electronic_ISBN :
1082-3409
Type :
conf
DOI :
10.1109/ICTAI.2011.34
Filename :
6103324
Link To Document :
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