2007 IEEE International Joint Conference on Neural Networks (IJCNN 2007), August 12-17, 2007, Orlando, Florida, USA
A method for the construction of Virtual Reality spaces for visual data mining using multi-objective optimization with genetic algorithms on 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 the computation of two unsupervised similarity structure preservation measures between the original data matrix and its image in the new space. A set of spaces is constructed from selected solutions along the Pareto front which enables the understanding of the internal properties of the data based on visual inspection of non-dominating spaces with different properties. This strategy represents a conceptual improvement over spaces computed by single-objective optimization. The presented approach is domain independent and is illustrated with an application to the study of hydrochemical properties of ice and water samples from the Arctic.
Proceedings of the 2007 IEEE International Joint Conference on Neural Networks (IJCNN 2007).