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.

在本文中，我们通过使用一些经典的机器学习模型、人工神经网络、k-最近邻、偏最小二乘判别分析、随机森林和支持向量机 (SVM) 来探索特征选择方法是否可以提高模型性能，结合特征选择方法、距离相关系数（DCC）、线性判别分析的重要权重（IW-LDA）和Relief-F算法，对八种木材（非洲紫檀、巴西布宾加、榆树、落叶松、缅甸）进行判别紫檀,紫檀、杨树和梧桐树）基于激光诱导击穿光谱 (LIBS) 技术。光谱数据通过最大线强度归一化，主成分分析应用于探索性数据分析。根据 DCC、IW-LDA 和 Relief-F 评估的重要权重选择特征谱线。所有模型都是通过使用不同数量的特征线（按其重要权重排序）作为输入来构建的。分析特征线数与模型正确分类率（CCR）的关系。通过使用合适的特征选择，所有模型的 CCR 都得到了改进。当用 IW-LDA 方法选择的 86 条特征线建立 SVM 模型时，最高 CCR 达到 (98.55...0.39)%。