Comparative Performances of Stochastic Competitive Evolutionary Neural Tree (SCENT) with Neural Classifiers

摘要：

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.

作者: W. Pensuwon R. G. Adams N. Davey

作者单位: Department of Computer Sciences University of Hertfordshire Hatfield, Herts, AL10 9AB, United Kingdo Department of Computer Sciences University of Hertfordshire Hatfield, Herts, AL10 9AB, United Kingdo

会议类型: 国际会议

会议名称: 8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)

会议地点: 上海

会议语种:英文

页码: 159-164

在线出版日期: 2001-11-14（万方平台首次上网日期，不代表论文的发表时间）