Abstract
A method for the construction of Virtual Reality
spaces for visual data mining using multi-objective
optimisation with genetic algorithms on non-linear
discriminant (NDA) neural networks is presented. Two
neural network layers (output and last hidden) are used
for the construction of simultaneous solutions for: a
supervised classification of data patterns and 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 optimisation. 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.
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