Architecture and performance of a stochastic competitive evolutionary neural tree network

N. Davey, R. G. Adams, S. J. George

Research output: Contribution to journalJournal Articlepeer-review

12 Citations (Scopus)


A new dynamic tree structured network - the Stochastic Competitive Evolutionary Neural Tree (SCENT) is introduced. The network is able to provide a hierarchical classification of unlabelled data sets. The main advantage that SCENT offers over other hierarchical competitive networks is its ability to self-determine the number and structure of the competitive nodes in the network without the need for externally set parameters. The network produces stable classificatory structures by halting its growth using locally calculated, stochastically controlled, heuristics. The performance of the network is analysed by comparing its results with that of a good non-hierarchical clusterer, and with three other hierarchical clusterers and its non stochastic predecessor. SCENT's classificatory capabilities are demonstrated by its ability to produce a representative hierarchical structure to classify a broad range of data sets.

Original languageEnglish
Pages (from-to)75-93
Number of pages19
JournalApplied Intelligence
Issue number1-2
Publication statusPublished - 2000


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