Multi-objective evolutionary optimization for constructing neural networks for virtual reality visual data mining: Application to geophysical prospecting

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TypeArticle
ConferenceNeural Networks Journal, May 2007.
Volume20
Issue4
Subjectvisual data mining; virtual reality; multi-objective optimization; neural networks; geophysical prospecting
AbstractA method for the construction of virtual reality spaces for visual data mining using multi-objective optimization with genetic algorithms on nonlinear discriminant (NDA) neural networks is presented. Two neural network layers (the output and the last hidden) are used for the construction of simultaneous solutions for: (i) a supervised classification of data patterns and (ii) an unsupervised similarity structure preservation between the original data matrix and its image in the new space. A set of spaces are constructed from selected solutions along the Pareto front. This strategy represents a conceptual improvement over spaces computed by single-objective optimization. In addition, genetic programming (in particular gene expression programming) is used for finding analytic representations of the complex mappings generating the spaces (a composition of NDA and orthogonal principal components). The presented approach is domain independent and is illustrated via application to the geophysical prospecting of caves.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number49317
NPARC number8914415
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Record identifierf84b5d3f-5c45-4b1c-81b9-3987caefa17d
Record created2009-04-22
Record modified2016-05-09
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