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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/748

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contributor.authorMisra, B B-
contributor.authorDehuri, S-
contributor.authorDash, P K-
contributor.authorPanda, G-
date.accessioned2008-11-21T03:43:18Z-
date.available2008-11-21T03:43:18Z-
date.issued2008-
identifier.citationIEEE Congress on Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). 1-6 June 2008 Page(s):2298 - 2306en
identifier.urihttp://dx.doi.org/10.1109/CEC.2008.4631104-
identifier.urihttp://hdl.handle.net/2080/748-
descriptionCopyright for the paper belongs to IEEEen
description.abstractIn this paper, we proposed a reduced polynomial neural swarm net (RPNSN) for the task of classification. Classification task is one of the most studied tasks of data mining. In solving classification task of data mining, the classical algorithm such as polynomial neural network (PNN) takes large computation time because the network grows over the training period (i.e. the partial descriptions (PDs) in each layer grows in successive generations). Unlike PNN our proposed network needs to generate the partial description for a single layer. Particle swarm optimization (PSO) technique is used to select a relevant set of PDs as well as features, which are then fed to the output layer of our proposed net which contain only one neuron. The selection mechanism used here is a kind of wrapper approach. Performance of this model is compared with the results obtained from PNN. Simulation result shows that the performance of RPNSN is encouraging for harnessing its power in data mining area and also better in terms of processing time than the PNN model.en
format.extent272951 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherIEEEen
subjectdata miningen
subjectneural netsen
subjectparticle swarm optimisationen
subjectpattern classificationen
subjectpolynomialsen
titleReduced polynomial neural swarm net for classification task in data miningen
typeArticleen
Appears in Collections:Conference Papers

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