A modified backward elimination approach for the rapid classification of Chinese ceramics using laser-induced breakdown spectroscopy and chemometrics. Issue 3 (21st January 2020) - Ruan, Fangqi; Hou, Lin; Zhang, Tianlong; Li, Hua
Abstract : A modified backward elimination approach was proposed for feature selection (FS) to eliminate the redundant and irrelevant features from laser-induced breakdown spectroscopy (LIBS) spectra for the rapid classification of Chinese archaeological ceramics. Abstract : A modified backward elimination approach was proposed for feature selection (FS) to eliminate the redundant and irrelevant features from laser-induced breakdown spectroscopy (LIBS) spectra for the rapid classification of Chinese archaeological ceramics. The major elements (Fe, Ca, Si, Al and Mg) were identified in the LIBS spectra of the ancient ceramics using the National Institute of Standards and Technology (NIST) database. Six different pre-processing methods were used to reduce the errors caused by random factors and the influence of various non-target factors on the classification results, which could increase the comparability among the Chinese archaeological ceramics from different dynasties. The input features for the random forest (RF) model were selected by a modified backward elimination approach and three assessment criteria, namely sensitivity, specificity and accuracy, from full spectra. LIBS spectra pre-processed by mean centering with the optimal input feature were used to construct an RF classification model for different dynasty ceramics. As indicated by the research results, the sensitivity, specificity and accuracy of the RF model for the ceramic samples in the test set are 0.9526, 0.9910 and 0.9782, respectively. In this sense, available statistics proved the excellent performance of Chinese archaeological ceramic classification. Compared with the predictive result using RF, VI-RF and SBS-RF models, the sensitivity, specificity and accuracy of the modified SBS-RF model are higher than the results by other models. The results demonstrate that the proposed algorithm is more efficient in reducing the redundant features and computational time and improve the model performance. In addition, it is a good alternative for rapid analyses in multivariate classification.