| Download | - View accepted manuscript: Multi-objective Evolutionary Optimization of Neural Networks for Virtual Reality Visual Data Mining: Application to Hydrochemistry (PDF, 577 KiB)
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| Author | Search for: Valdés, Julio; Search for: Barton, Alan |
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| Format | Text, Article |
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| Conference | 2007 IEEE International Joint Conference on Neural Networks (IJCNN 2007), August 12-17, 2007, Orlando, Florida, USA |
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| Abstract | 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. |
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| Publication date | 2007 |
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| In | |
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| Language | English |
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| NRC number | NRCC 49296 |
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| NPARC number | 8914091 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 03d3a799-fc27-4919-828b-9078809b0199 |
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| Record created | 2009-04-22 |
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| Record modified | 2020-08-12 |
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