An ensemble of Monte Carlo uninformative variable elimination for wavelength selection

Han, Qing-Juan; Wu, Hai‐Long; Cai, Chen-Bo; Xu, Lu; Yu, Ru‐Qin

doi:10.1016/j.aca.2008.02.032

Cited by 155 publications

(77 citation statements)

References 23 publications

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“…Uninformative variable elimination (UVE) is a widely used method for variable selection in chemometrics [26]. Its extended version, Monte Carlo UVE (MCUVE), was recently proposed [27,31]. Mimicking the principle of "survival of the fittest" in Darwin's evolution theory, we developed a variable selection method in our previous work, called competitive adaptive reweighted sampling (CARS) [8,28,32,33], which was shown to have the potential to identify an optimal subset of variables that show high predictive performances.…”

Section: Comparison Of Predictive Performances Of Variables Subsetsmentioning

confidence: 99%

Model Population Analysis for Statistical Model Comparison

Li¹,

Liang²,

Xu³

2012

Chemometrics in Practical Applications

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Section: Comparison Of Predictive Performances Of Variables Subsetsmentioning

confidence: 99%

Model Population Analysis for Statistical Model Comparison

Li¹,

Liang²,

Xu³

2012

Chemometrics in Practical Applications

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“…Among them, samples 11-20 are the remaining 10 genuine samples. As the 47 samples have been sorted according to their Bacteriostatic Rate, the last three samples with low bacteriostatic activity are samples [21][22][23].…”

Section: New Similarity Analysis Based On Characteristic Variablesmentioning

confidence: 99%

“…4,6 A selection procedure that optimizes the prediction capacity will detect the most relevant variables for the analyte of interest and remove the irrelevant variables. 18 There are several variable selection methods, like genetic algorithm (GA), 19 interval PLS (iPLS), 20 uninformative variable elimination (UVE), 21 montecarlo-uninformative variable elimination (MC-UVE), 22 successive projection algorithm (SPA) 23 and so on. These algorithms are used to identify and select the variables that improve the performances of multivariate calibration models, and these significant variables could be used for the calculation of similarity.…”

Section: Introductionmentioning

confidence: 99%

Near Infrared Spectral Similarity Combined with Variable Selection Method in the Quality Control of Flos Lonicerae: A Preliminary Study

Meng

et al. 2011

Chin. J. Chem.

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This paper indicates the possibility to use near infrared (NIR) spectral similarity as a rapid method to estimate the quality of Flos Lonicerae. Variable selection together with modelling techniques is utilized to select representative variables that are used to calculate the similarity. NIR is used to build calibration models to predict the bacteriostatic activity of Flos Lonicerae. For the determination of the bacteriostatic activity, the in vitro experiment is used. Models are built for the Gram-positive bacteria and also for the Gram-negative bacteria. A genetic algorithm combined with partial least squares regression (GA-PLS) is used to perform the calibration. The results of GA-PLS models are compared to interval partial least squares (iPLS) models, full-spectrum PLS and full-spectrum principal component regression (PCR) models. Then, the variables in the two GA-PLS models are combined and then used to calculate the NIR spectral similarity of samples. The similarity based on the characteristic variables and full spectrum is used for evaluating the fingerprints of Flos Lonicerae, respectively. The results show that the combination of variable selection method, modelling techniques and similarity analysis might be a powerful tool for quality control of traditional Chinese medicine (TCM).

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“…The main drawback of ultraviolet-visible (UV-VIS) is its poor selectivity because in many cases UV-VIS spectra display strong overlaps, especially some less specific and selective chromagenic reagents often give rise to strongly overlapped spectra in many cases. The combination of artificial intelligence methods with the computer-controlled spectrophotometers was proven to be effective in overcoming this difficulty [1][2][3]. Artificial neural network (ANN) is a form of artificial intelligence that mathematically simulates biological nervous system [4,5].…”

mentioning

confidence: 99%

Processing of spectrophotometric array signals using an artificial intelligence method

Gao¹,

Ren²

2013

Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)

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Abstract-Thispaper addresses processing of spectrophotometric array signals based on genetic algorithms (GA) least square support vector machines (LS-SVM) regression to provide a powerful model for machine learning and data mining. The key to complete LS-SVM regression is to choose its optimal parameters. Due to their outstanding ability in solving global optimization problems in complex multidimensional search space, GA are used in this study to obtain the optimal parameter combination of the LS-SVM model. Experimental results showed the GA-LS-SVM method to be successful for simultaneous multicomponent determination even where severe overlap of spectra was present.

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An ensemble of Monte Carlo uninformative variable elimination for wavelength selection

Cited by 155 publications

References 23 publications

Model Population Analysis for Statistical Model Comparison

Model Population Analysis for Statistical Model Comparison

Near Infrared Spectral Similarity Combined with Variable Selection Method in the Quality Control of Flos Lonicerae: A Preliminary Study

Processing of spectrophotometric array signals using an artificial intelligence method

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