Resumen de Novel feature selection method based on Stochastic Methods Coupled to Support Vector Machines using H- NMR data (data of olive and hazelnut oils)
Oscar Eduardo Gualdron, Claudia Isaza, Cristhian Manuel Duran
Abstract 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. Método de selección de variables basados en métodos estocásticos acoplados a Maquinas de soporte vectorial usando datos de H-NMR (data de aceite de oliva y avellana)
