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An enhanced swarm intelligence clustering-based
RBFNN classifier and its application in deep Web sources classification |
| Yong FENG,Zhongfu WU,Jiang ZHONG,Chunxiao YE,Kaigui WU, |
| College of Computer Science,
Chongqing University, Chongqing 400030, China; |
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Abstract 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.
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| Keywords
swarm intelligence
Clustering
radial basis function neural network (RBFNN)
deep Web sources classification
classifier
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Issue Date: 05 December 2010
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Chen S, Cowan C N, Grant P M. Orthogonal least squares learning algorithm for radialbasis function networks. IEEE Transactionson Neural Networks, 1991, 2(2): 302―309
doi: 10.1109/72.80341
|
|
Mao K Z, Huang G B. Neuron selection for RBFneural network classifier based on data structure preserving criterion. IEEE Transactions on Neural Networks, 2005, 16(6): 1531―1540
doi: 10.1109/TNN.2005.853575
|
|
Huang G B, Saratchandran P, Sundararajan N. A generalized growing and pruning RBF (GGAP-RBF) neuralnetwork for function approximation. IEEETransactions on Neural Networks, 2005, 16(1): 57―67
doi: 10.1109/TNN.2004.836241
|
|
Lee S J, Hou C L. An ART-based constructionof RBF networks. IEEE Transactions on NeuralNetworks, 2002, 13(6): 1308―1321
doi: 10.1109/TNN.2002.804308
|
|
Lee H M, Chen C M, Lu Y F. A self-organizing HCMAC neural-network classifier. IEEE Transactions on Neural Networks, 2003, 14(1): 15―27
doi: 10.1109/TNN.2002.806607
|
|
Gonzalez J, Rojas I, Ortega J, Pomares H, Fernandez F J, Diaz A F. Multiobjective evolutionary optimization of the size,shape, and position parameters of radial basis function networks forfunction approximation. IEEE Transactionson Neural Networks, 2003, 14(6): 1478―1495
doi: 10.1109/TNN.2003.820657
|
|
Karayiannis N B. Reformulated radial basis neural networks trained by gradient descent. IEEE Transactions on Neural Networks, 1999, 10(3): 657―671
doi: 10.1109/72.761725
|
|
Randolph-Gips M M, Karayiannis N B. Cosine radial basis functionneural networks. In: Proceedings of theInternational Joint Conference on Neural Networks. New York: IEEE Press, 2003, 96―101
|
|
Deneuhourg J L, Goss S, Franks N. The dynamics of collective sorting: robot-like ant andant-like robot. In: Proceedings of the1st international conference on simulation of adaptive behavior onfrom animals to animats. Cambridge: MIT Press, 1991, 356―365
|
|
Lumer E, Faieta B. Diversity and adaptationin populations of clustering ants. In: Proceedings of the third international conference on Simulation ofadaptive behavior: from animals to animats 3: from animals to animats3. Cambridge: MIT Press, 1994, 499―508
|
|
Bonabeau E. DorigoM Swarm intelligence: from natural to artificial systems: Oxford UniversityPress, 1999
|
|
Wu B, Shi Z Z. A clustering algorithm basedon swarm intelligence. In: Proceeding ofInternational Conferences on Info-tech & Info-net. New York: IEEE Press, 2001, 58―66
|
|
Ramos V, Pina P, Muge F. Self-organized data and image retrieval as a consequenceof inter-dynamic synergistic relationships in artificial ant colonies. In: Frontiers in Artificial Intelligence and Applications,Soft Computing Systems-Design, Management and Applications. Amsterdam: IOS Press, 2002, 500―509
|
|
Ramos V, Merelo J J. Self-organized stigmergicdocument maps: environment as a mechanism for context learning. In: Proceeding of 1st Spanish Conference on Evolutionaryand Bio-Inspired Algorithms. Spain: Merida University Press, 2002, 284―293
|
|
Han Y F, Shi P F. An improved ant colony algorithmfor fuzzy clustering in image segmentation. Neurocomputing, 2007, 70(4―6): 665―671
|
|
Runkler T A. Ant colony optimization of clustering models. International Journal of Intelligent Systems, 2005, 20(12): 1233―1251
doi: 10.1002/int.20111
|
|
Kuo R J, Wang H S, Hu T L, Chou S H. Applicationof ant k-means on clustering analysis. Computers & Mathematics with Applications (Oxford, England), 2005, 50(10―12): 1709―1724
doi: 10.1016/j.camwa.2005.05.009
|
|
Chen A P, Chen C C. A new efficient approachfor data clustering in electronic library using ant colony clusteringalgorithm. Electronic Library, 2006, 24(4): 548―559
doi: 10.1108/02640470610689223
|
|
Feng Y, Zhong J, Xiong Z Y, Ye C X, Wu K G. Network anomaly detection based on DSOMand ACO clustering. Lecture Notes in Computer Science.Advances in Neural Networks – ISNN 2007. LNCS, 2007, 4492(Part 2): 947―955
|
|
He B, Patel M, Zhang Z, Chang K C C. Accessingthe deep Web. Communications of the ACM, 2007, 50(5): 94―101
doi: 10.1145/1230819.1241670
|
|
Ghanem T M, Aref W G. Databases deepen the Web. Computer, 2004, 37(1): 116―117
doi: 10.1109/MC.2004.1260731
|
|
Liu W, Meng X F, Meng W Y. A survey of deep Web data integration. Chinese Journal of Computers, 2007, 30(9): 1475―1489 (in Chinese)
|
|
Wang Y, Zuo W L, Peng T, He F L. Domain-specificdeep Web sources discovery. In: Proceedingof Fourth International Conference on Natural Computation, New York: IEEE Press, 2008, 202―206
doi: 10.1109/ICNC.2008.350
|
|
, 2003metaquerier.cs.uiuc.edu/repository, 2003
|
|
He B, Chang K C C. Automatic complex schemamatching across Web query interfaces: a correlation mining approach. ACM Transactions on Database Systems, 2006, 31(1): 346―395
doi: 10.1145/1132863.1132872
|
|
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