Near infrared spectrum variable selection method based on self-weighted variable combination cluster analysis

A near-infrared spectrum and variable selection technology, which is applied in the field of near-infrared spectrum variable selection based on self-weighted variable combination cluster analysis, can solve the problems of low prediction accuracy of the algorithm model, many unstable factors, and unstable prediction accuracy of the model. Achieve the effect of improving prediction accuracy, improving stability and reliability, and avoiding overfitting

Active Publication Date: 2017-07-14
CHANGCHUN JINGYI PHOTOELECTRIC TECH CO LTD
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Problems solved by technology

[0004] Although a large number of variable selection methods have been proposed, each variable selection method only uses one of these information vectors as the basis for judging the importance of variables, and then ignores the influence of other information vectors on the predi

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  • Near infrared spectrum variable selection method based on self-weighted variable combination cluster analysis
  • Near infrared spectrum variable selection method based on self-weighted variable combination cluster analysis
  • Near infrared spectrum variable selection method based on self-weighted variable combination cluster analysis

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Embodiment Construction

[0036] Embodiment 1: In order to prove the applicability of the present invention, a detailed description will be given in conjunction with examples. However, the present invention can also be applied to spectral data other than the example used here.

[0037] figure 1 It is a flow chart based on the self-weighting variable combination cluster analysis method (AWVCPA) algorithm provided by the present invention, as seen, the present invention specifically comprises the following steps:

[0038] (1) The collected corn near-infrared spectrum data includes 80 corn samples, and the near-infrared spectrum wavelength of each sample is distributed in 1100-2498nm. The near-infrared spectrum of each corn sample is tested by a spectrometer, and each corn sample is tested by chemical methods. The chemical value of the oil content of the sample. Using the Kennard-Stone (K-S) method, 60 samples of spectral data and chemical value data were selected as calibration sets to establish a pred...

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Abstract

The invention relates to a near infrared spectrum variable selection method based on self-weighted variable combination cluster analysis, which belongs to the technical field of nondestructive analysis in the field of analytic chemistry. The specific implementation process is as follows: firstly, a variable space is randomly sampled through a binary matrix sampling method (BMS); secondly, two types of information vectors (IVs) of variables, i.e., occurrence frequencies (Fre) and partial least square regression coefficients (Reg), are weighted, so that a contribution value of each spectral variable is obtained, and thereby the influence of the two types of IVs, i.e., Fre and Reg, on variable importance is taken into consideration. Finally, the variables with relatively small contribution values are deleted through an exponential decay function (EDF), and thereby feature variable selection is realized. Compared with the prior art, the method has the advantages of quickness and repeatability, and increases the prediction precision and stability of a model.

Description

technical field [0001] The invention of the method belongs to the technical field of non-destructive analysis in the field of analytical chemistry, and specifically relates to a near-infrared spectrum variable selection method based on self-weighting variable combination cluster analysis. Background technique [0002] With the development of near-infrared spectroscopy and chemometrics, variable selection technology has become a key link in the field of near-infrared spectroscopy to analyze high-dimensional data. Variable selection for spectral variables can improve the predictive ability of prediction models. Reduce spectral data dimensionality and enhance predictive model interpretability. At the same time, variable selection is also a very challenging problem, and it is a very difficult problem to find a set of optimal variable combinations as the variable space increases. [0003] Common variable selection methods at home and abroad include Uninformative variables elimin...

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Application Information

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IPC IPC(8): G01N21/359
CPCG01N21/359
Inventor 宦克为韩雪艳刘小溪赵环石晓光
Owner CHANGCHUN JINGYI PHOTOELECTRIC TECH CO LTD
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