An enhanced swarm intelligence clustering-based RBFNN classifier and its application in deep Web sources classification

The central problem in training a radial basis function neural network (RBFNN) is the selection of hidden layer neurons, which includes the selection of the center and width of those neurons. In this paper, we propose an enhanced swarm intelligence clustering (ESIC) method to select hidden layer neu...

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Bibliographic Details
Published in:Frontiers of Computer Science 2010-12, Vol.4 (4), p.560-570
Main Authors: Feng, Yong, Wu, Zhongfu, Zhong, Jiang, Ye, Chunxiao, Wu, Kaigui
Format: Article
Language:English
Subjects:
Classification
Classifiers
Clustering
Computer Science
Neural networks
Neurons
Radial basis function
Research Article
Support vector machines
Swarm intelligence
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Summary:The central problem in training a radial basis function neural network (RBFNN) is the selection of hidden layer neurons, which includes the selection of the center and width of those neurons. In this paper, we propose an enhanced swarm intelligence clustering (ESIC) method to select hidden layer neurons, and then, train a cosine RBFNN based on the gradient descent learning process. Also, we apply this new method for classification of deep Web sources. Experimental results show that the average Precision, Recall and F of our ESIC-based RBFNN classifier achieve higher performance than BP, Support Vector Machines (SVM) and OLS RBF for our deep Web sources classification problems.
ISSN:1673-7350
2095-2228
1673-7466
2095-2236
DOI:10.1007/s11704-010-0104-5