Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

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• 作者：Varun Kumar Ojha^a ; ^{varun.kumar.ojha@vsb.cz} ; Ajith Abraham^b ; ^{ajith.abraham@ieee.org} ; Vá ; clav Sná ; &scaron ; el^a ; ^{vaclav.snasel@vsb.cz}
• 关键词：Pareto-based multiobjectives ; Flexible neural tree ; Ensemble ; Approximation ; Feature selection
• 刊名：Applied Soft Computing
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：52
• 期：Complete
• 页码：909-924
• 全文大小：2856 K
• 卷排序：52

文摘

A heterogeneous flexible neural tree (FNT) for function approximation was proposed. FNT was studied under Pareto-based multiobjective genetic programming framework. A diversity-index was introduced to maintain diversity in genetic population. FNT was found competitive with other algorithm when cross validated over datasets. Evolutionary weighted ensemble of HFNTs further improved FNT performance.