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Parallel EDAs to create multivariate calibration models for quantitative chemical applications
- Source :
-
Journal of Parallel & Distributed Computing . Aug2006, Vol. 66 Issue 8, p1002-1013. 12p. - Publication Year :
- 2006
-
Abstract
- Abstract: This paper describes the application of a collection of data mining methods to solve a calibration problem in a quantitative chemistry environment. Experimental data obtained from reactions which involve known concentrations of two or more components are used to calibrate a model that, later, will be used to predict the (unknown) concentrations of those components in a new reaction. This problem can be seen as a one, where the goal is to obtain good values for the variables to predict while minimizing the number of the input variables needed, taking a small subset of really significant ones. Initial approaches to the problem were principal components analysis and filtering combined with two prediction techniques: artificial neural networks and partial least squares regression. Finally, a parallel estimation of distribution algorithm was used to reduce the number of variables to be used for prediction, yielding the best models for all the considered problems. [Copyright &y& Elsevier]
- Subjects :
- *DATA mining
*CHEMICAL reactions
*ARTIFICIAL neural networks
*ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 07437315
- Volume :
- 66
- Issue :
- 8
- Database :
- Academic Search Index
- Journal :
- Journal of Parallel & Distributed Computing
- Publication Type :
- Academic Journal
- Accession number :
- 21513352
- Full Text :
- https://doi.org/10.1016/j.jpdc.2006.03.001