Laser-induced breakdown spectroscopy for the classification of wood materials using machine learning methods combined with feature selection

In this paper, we explore whether a feature selection method can improve model performance by using some classical machine learning models, artificial neural network, k-nearest neighbor, partial least squares-discrimination analysis, random forest, and support vector machine (SVM), combined with the feature selection methods, distance correlation coefficient (DCC), important weight of linear discriminant analysis (IW-LDA), and Relief-F algorithms, to discriminate eight species of wood (African rosewood, Brazilian bubinga, elm, larch, Myanmar padauk, Pterocarpus erinaceus, poplar, and sycamore) based on the laser-induced breakdown spectroscopy (LIBS) technique. The spectral data are normalized by the maximum of line intensity and principal component analysis is applied to the exploratory data analysis. The feature spectral lines are selected out based on the important weight assessed by DCC, IW-LDA, and Relief-F. All models are built by using the different number of feature lines (sorted by their important weight) as input. The relationship between the number of feature lines and the correct classification rate (CCR) of the model is analyzed. The CCRs of all models are improved by using a suitable feature selection. The highest CCR achieves (98.55⋯0.39)% when the SVM model is established from 86 feature lines selected by the IW-LDA method. The result demonstrates that a suitable feature selection method can improve model recognition ability and reduce modeling time in the application of wood materials classification using LIBS.