Iteratively variable subset optimization for multivariate calibration

RSC Advances ◽  
2015 ◽  
Vol 5 (116) ◽  
pp. 95771-95780 ◽  
Author(s):  
Weiting Wang ◽  
Yonghuan Yun ◽  
Baichuan Deng ◽  
Wei Fan ◽  
Yizeng Liang
Keyword(s):  
Variable Selection ◽  
Multivariate Calibration ◽  
Selection Method ◽  
Good Prediction ◽  
Variable Selection Method ◽  
Variable Subset

IVSO is a variable selection method, which shows good prediction and stability and can eliminate uninformative variables gradually and gently.

scholarly journals An efficient variable selection method based on random frog for the multivariate calibration of NIR spectra

RSC Advances ◽  
2020 ◽  
Vol 10 (28) ◽  
pp. 16245-16253
Author(s):  
Jingjing Sun ◽  
Wude Yang ◽  
Meichen Feng ◽  
Qifang Liu ◽  
Muhammad Saleem Kubar
Keyword(s):  
Variable Selection ◽  
Multivariate Calibration ◽  
Variable Interval ◽  
Selection Method ◽  
Variable Selection Method

A variable selection method based on random frog, variable filtering and variable interval expansion.

Predictive and Descriptive CoMFA Models: The Effect of Variable Selection

2018 ◽  
Vol 21 (2) ◽  
pp. 117-124 ◽  
Author(s):  
Bakhtyar Sepehri ◽  
Nematollah Omidikia ◽  
Mohsen Kompany-Zareh ◽  
Raouf Ghavami
Keyword(s):  
Variable Selection ◽  
Predictive Power ◽  
Selection Method ◽  
Data Sets ◽  
Data Set ◽  
Comfa Model ◽  
Variable Selection Method

Aims & Scope: In this research, 8 variable selection approaches were used to investigate the effect of variable selection on the predictive power and stability of CoMFA models. Materials & Methods: Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors and 57 ATAD2 bromodomain inhibitors were modelled by CoMFA. First of all, for all three data sets, CoMFA models with all CoMFA descriptors were created then by applying each variable selection method a new CoMFA model was developed so for each data set, 9 CoMFA models were built. Obtained results show noisy and uninformative variables affect CoMFA results. Based on created models, applying 5 variable selection approaches including FFD, SRD-FFD, IVE-PLS, SRD-UVEPLS and SPA-jackknife increases the predictive power and stability of CoMFA models significantly. Result & Conclusion: Among them, SPA-jackknife removes most of the variables while FFD retains most of them. FFD and IVE-PLS are time consuming process while SRD-FFD and SRD-UVE-PLS run need to few seconds. Also applying FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS protect CoMFA countor maps information for both fields.

scholarly journals Variable Selection in the Regularized Simultaneous Component Analysis Method for Multi-Source Data Integration

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Zhengguo Gu ◽  
Niek C. de Schipper ◽  
Katrijn Van Deun
Keyword(s):  
Variable Selection ◽  
Data Integration ◽  
Component Analysis ◽  
Selection Method ◽  
Gps Data ◽  
Positioning Systems ◽  
Variable Selection Method ◽  
Diary Data ◽  
Simultaneous Component Analysis ◽  
Travel Diary

AbstractInterdisciplinary research often involves analyzing data obtained from different data sources with respect to the same subjects, objects, or experimental units. For example, global positioning systems (GPS) data have been coupled with travel diary data, resulting in a better understanding of traveling behavior. The GPS data and the travel diary data are very different in nature, and, to analyze the two types of data jointly, one often uses data integration techniques, such as the regularized simultaneous component analysis (regularized SCA) method. Regularized SCA is an extension of the (sparse) principle component analysis model to the cases where at least two data blocks are jointly analyzed, which - in order to reveal the joint and unique sources of variation - heavily relies on proper selection of the set of variables (i.e., component loadings) in the components. Regularized SCA requires a proper variable selection method to either identify the optimal values for tuning parameters or stably select variables. By means of two simulation studies with various noise and sparseness levels in simulated data, we compare six variable selection methods, which are cross-validation (CV) with the “one-standard-error” rule, repeated double CV (rdCV), BIC, Bolasso with CV, stability selection, and index of sparseness (IS) - a lesser known (compared to the first five methods) but computationally efficient method. Results show that IS is the best-performing variable selection method.

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