Citation

Suárez Sánchez A, Riesgo Fernández P, Sánchez Lasheras F, de Cos Juez FJ, García Nieto PJ. Appl Math Comput 2011; 218(7): 3539-3552.

Copyright

(Copyright © 2011, Elsevier Publishing)

DOI

10.1016/j.amc.2011.08.100

PMID

unavailable

Abstract

Support vector machines (SVMs), which are a kind of statistical learning methods, were applied in this research work to predict occupational accidents with success. In the first place, semi-parametric principal component analysis (SPPCA) was used in order to perform a dimensional reduction, but no satisfactory results were obtained. Next, a dimensional reduction was carried out using an innovative and intelligent computing regression algorithm known as multivariate adaptive regression splines (MARS) model with good results. The variables selected as important by the previous MARS model were taken as input variables for a SVM model. This SVM technique was able to classify, according to their working conditions, those workers that have suffered a work-related accident in the last 12 months and those that have not. SVM technique does not over-fit the experimental data and gives place to a better performance than back-propagation neural network models. Finally, the results and conclusions of this study are presented.

Language: en