Comparative performances of stochastic competitive evolutionary neural tree (SCENT) with neural classifiers

W. Pensuwon, R.G. Adams, N. Davey

Research output: Contribution to conferencePaperpeer-review

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Abstract

A stochastic competitive evolutionary neural tree (SCENT) is described and evaluated against the best neural classifiers with equivalent functionality, using a collection of data sets chosen to provide a variety of clustering scenarios. SCENT is firstly shown to produce flat classifications at least as well as the other two neural classifiers used. Moreover its variability in performance over the data sets is shown to be small. In addition SCENT also produces a tree that can show any hierarchical structure contained in the data. For two real world data sets the tree captures hierarchical features of the data.
Original languageEnglish
Pages121-126
Publication statusPublished - 2001
EventInt Conf on Neural Information Processing (ICONIP 2001) - Shanghai, China
Duration: 14 Nov 200118 Nov 2001

Conference

ConferenceInt Conf on Neural Information Processing (ICONIP 2001)
Country/TerritoryChina
CityShanghai
Period14/11/0118/11/01

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