Abstract

GUALDRON, Oscar Eduardo; CLAUDIA, Isaza  and  DURAN, Cristhian Manuel. Novel feature selection method based on Stochastic Methods Coupled to Support Vector Machines using H- NMR data (data of olive and hazelnut oils). Rev. ion [online]. 2014, vol.27, n.2, pp.17-28. ISSN 0120-100X.

One of the principal inconveniences that analysis and information processing presents is that of the representation of dataset. Normally, one encounters a high number of samples, each one with thousands of variables, and in many cases with irrelevant information and noise. Therefore, in order to represent findings in a clearer way, it is necessary to reduce the amount of variables. In this paper, a novel variable selection technique for multivariable data analysis, inspired on stochastic methods and designed to work with support vector machines (SVM), is described. The approach is demonstrated in a food application involving the detection of adulteration of olive oil (more expensive) with hazelnut oil (cheaper). Fingerprinting by H NMR spectroscopy was used to analyze the different samples. Results show that it is possible to reduce the number of variables without affecting classification results.

Keywords : feature selection; H-NMR; simulated annealing; support vector machine; olive oil; hazelnut oil.

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