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Multi-objective Evolutionary Optimization of Neural Networks for Virtual Reality Visual Data Mining: Application to Hydrochemistry

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2 Author(s)
Valdes, J.J. ; Inst. for Inf. Technol., Ottawa ; Barton, A.J.

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.

Published in:

Neural Networks, 2007. IJCNN 2007. International Joint Conference on

Date of Conference:

12-17 Aug. 2007

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