Diversity creation in local search for the evolution of neural network ensembles

Pete DUELL, Iris FERMIN, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

2 Citations (Scopus)

Abstract

The EENCL algorithm [1] automatically designs neural network ensembles for classification, combining global evolution with local search based on gradient descent. Two mechanisms encourage diversity: Negative Correlation Learning (NCL) and implicit fitness sharing. This paper analyses EENCL, finding that NCL is not an essential component of the algorithm, while implicit fitness sharing is. Furthermore, we find that a local search based on independent training is equally effective in both accuracy and diversity. We propose that NCL is unnecessary in EENCL for the tested datasets, and that complementary diversity in local search and global evolution may lead to better ensembles. © 2006 i6doc.com publication. All rights reserved.
Original languageEnglish
Title of host publicationESANN 2006 Proceedings - European Symposium on Artificial Neural Networks
Pages431-436
Number of pages6
Publication statusPublished - 2006
Externally publishedYes

Bibliographical note

This work is partially funded by EPSRC and Thales Research & Technology (UK) Ltd.

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