Parallel and Multi-Objective EDAs to Create Multivariate Calibration Models for Quantitative Chemical Applications

Authors:
A. Mendiburu;J. Miguel-Alonso;J. A. Lozano;M. Ostra;C. Ubide
Affiliations:
University of the Basque Country;University of the Basque Country;University of the Basque Country;University of the Basque Country;University of the Basque Country
Venue:
ICPPW '05 Proceedings of the 2005 International Conference on Parallel Processing Workshops
Year:
2005

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Parallel EDAs to create multivariate calibration models for quantitative chemical applications

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Abstract

This paper describes the application of several data mining approaches 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 selection + prediction 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. Then we used methods to make smarter reductions of variables, by means of parallel Estimation of Distribution Algorithms (EDAs) to choose collections of variables that yield models with less average prediction errors. Afinal step was to use multi-objective parallel EDAs, in order to present a set of optimal solutions instead of a single solution.