An ensemble of Monte Carlo uninformative variable elimination for wavelength selection
作者： Qing-Juan Han , Hai-Long Wu , Chen-Bo Cai , Lu Xu , Ru-Qin Yu
DOI: 10.1016/J.ACA.2008.02.032
关键词:
摘要: An improved method based on an ensemble of Monte Carlo uninformative variable elimination (EMCUVE) is presented for wavelength selection in multivariate calibration spectral data. The proposed algorithm introduces (MC) strategy to elimination-PLS (UVE-PLS) instead leave-one-out estimating the contributions each PLS model. In EMCUVE variables are evaluated by different (MCUVE) models. Moreover, a fusion MCUVE and vote rule can obtain improvement over original method. Results obtained from simulated data real sets demonstrate that properly carry out course analysis improve predictive ability
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