Randomized smart subset selection for high-dimensional generalized linear models with false positive control

This study adopts two approaches to analyze the occurrence of algae at Haman Weir for Nakdong River; one is the traditional statistical method, such as logistic regression, while the other is machine learning technique, such as kNN, ANN, RF, Bagging, Boosting, and SVM. In order to compare the performance of the models, this study measured the accuracy, specificity, sensitivity, and AUC, which are representative model evaluation tools. The ROC curve is created by plotting association of sensitivity and (1‐specificity). The AUC that is area of ROC curve represents sensitivity and specificity. This measure has two competitive advantages compared to other evaluation tools. One is that it is scale‐invariant. It means that purpose of AUC is how well the model predicts. The other is that the AUC is classification‐threshold‐invariant. It shows that the AUC is independent of threshold because it is plotted association of sensitivity and (1‐specificity) obtained by threshold. We chose AUC as a final model evaluation tool with two advantages. Also, variable selection was conducted using the Boruta algorithm. In addition, we tried to distinguish the better model by comparing the model with the variable selection method and the model without the variable selection method. As a result of the analysis, Boruta algorithm as a variable selection method suggested PO4‐P, DO, BOD, NH3‐N, Susp, pH, TOC, Temp, TN, and TP as significant explanatory variables. A comparison was made between the model with and without these selected variables. Among the models without variable selection method, the accuracy of RF analysis was highest, and ANN analysis showed the highest AUC. In conclusion, ANN analysis using the variable selection method showed the best performance among the models with and without variable selection method.

Objective To evaluate the performance of three antibody kits for novel coronavirus (SARS-CoV-2) and to investigate the feasibility and advantages of them in clinical application. Methods A total of 104 patients who were admitted to Guangzhou Eighth People's Hospital with COVID-19 from January to February 2020 were selected as research group. Fifty-one healthy subjects were selected during the same period as negative control group. Serum antibodies (IgM/IgG) against SARS-CoV-2 were detected using two kinds of colloidal gold kits (A and B kits) and one chemiluminescence kit (C kit). The positive rates of SARS-CoV-2 nucleic acid in different samples from patients with COVID-19 were retrospectively analyzed. Results The clinical sensitivity of A kit to detect SARS-CoV-2-specific IgM and IgG was 77.88% (81/104) and 65.38% (68/104), respectively, and the clinical specificity was 70.59% (36/51) and 100.00% (51/51). However, the false positive rate in IgM detection was as high as 29.41% (15/51). The sensitivity of B kit to test total antibodies to SARS-CoV-2 was 63.46% (66/104), and the clinical specificity was 94.12% (48/51). The clinical sensitivity of C kit to detect SARS-CoV-2-specific IgM and IgG were respectively 31.73% (33/104) and 64.42% (67/104), and the clinical specificity were both 98.04% (50/51). There was a moderate correlation between the detection results of two colloidal gold kits and the chemiluminescence kit with the Kappa values of 0.462 and 0.587 (Z=6.157, P<0.01; Z=7.345, P<0.01). C kit had the highest positive detection rate for IgG, and would be more reliable to be used for IgG detection in COVID-19 patients 14 d after onset. The total positive detection rate of nucleic acid in all types of samples was 63.46% (66/104). The highest positive detection rate was in throat swabs or sputum samples, followed by those in blood samples and anal swabs. No viral nucleic acid was detected in urine samples for the time being. Conclusions SARS-CoV-2-specific antibodies could be detected in the early or late stage of COVID-19. The method of antibody detection has the advantages of shorter detection time, simple operation and high biological safety, indicating that it could be used as a supplementary or auxiliary detection for the diagnosis of suspected COVID-19 cases with negative nucleic acid test results. The chemiluminescence kit has good sensitivity and specificity, and is well recommended for clinical laboratories. Key words: SARS-CoV-2; Chemiluminescence immunoassay; Colloidal gold immunoaasay; Nucleic acid detection; Performance evaluation; Clinical application

Variable selection can improve the robustness and prediction accuracy of partial least squares (PLS) regression models and decrease the calculation time by selecting the optimal subset of variables in multivariate calibration. In this study, the performance of two variable selection methods for wavelength interval and individual wavelength coupled with partial least squares regression are investigated by employing the experimental data of asiaticoside (AS) and madecassoside (MS) contents in centella total glucosides (CTG) and a public dataset of corn. The studied variable selection methods include interval partial least squares regression ( iPLS), backward interval partial least squares ( biPLS), synergy interval partial least squares regression ( siPLS), competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE) and variable importance in projection (VIP). The results show that the implementation of variable selection methods improved the performance of the model compared with full-spectrum modeling. All variable selection methods improved the prediction of AS or MS contents in CTG. When latent variables for PLS models are less than 10 in the practical application, the RPD value of AS models by iPLS method is 7.5, and the RPD value of MS models by biPLS method is 2.9. The results of wavelength interval selection are better than individual wavelength selection, especially for iPLS and biPLS. The same results were obtained with the public data for moisture in corn, and the RPD value of biPLS model of moisture is 1.6. Therefore, the wavelength interval selection methods, such as iPLS or biPLS, are appropriate for improving the PLS model’s accuracy and robustness to determine the target components’ contents in solid samples.

This paper evaluates the performance of four variable selection methods suitable for case-control studies. Two of the methods are logistic regression and the rank transformed version of it which uses the ranks of the explanatory variables in place of the original observations. The third method is based on Kendall's tau b correlations. I propose a fourth method, a sign score regression model to select variables. To evaluate these four methods, I generate many data sets for a case group and a control group with the use of several different distributions and covariance matrices. I evaluate the methods on their ability to select correctly the variables related to case-control status while not selecting the unrelated variables. Using this criterion, the sign score regression method and the tau b method are more effective than the other two methods with uncorrelated or weakly correlated variables. The sign score regression method is more effective than the tau b method for all simulations that use normal variables and for some that use log-normal variables. Overall, the sign score regression method is the most effective variable selection method for data sets that have low or moderate correlations between variables.

Heuristics for unrooted, unranked, and ranked anomaly zones under birth-death models

An overview of variable selection methods in multivariate analysis of near-infrared spectra

Modelling soil thickness using environmental attributes in karst watersheds

Comparison of several variable selection methods for quantitative analysis and monitoring of the Yangxinshi tablet process using near-infrared spectroscopy

In this paper, an efficient variable and subset selection approach related to modified partial least squares (PLS) regression are proposed and applied to numerical and industry application to show the superior of the results. The main purpose of the proposed variable selection approach is to offer further useful information to identification, control as well as diagnosis. The significant advantage of the modified PLS-based variable selection method is to keep the superior fitting and prediction performance as well as the significantly reduced number of selected variables. To this aim, the canonical and modified PLS regression methods have been introduced. Then an effective and precise variable selection method which can select the most significant elements among huge redundant information is proposed. To this end, the effectiveness of the proposed variable selection method is demonstrated by the simulation results of a numerical instance and a process industry case study.

BackgroundThe redundancy of information is becoming a critical issue for epidemiologists. High-dimensional datasets require new effective variable selection methods to be developed. This study implements an advanced evolutionary variable selection method which is applied for cardiovascular predictive modeling. The epidemiological follow-up study KIHD (Kuopio Ischemic Heart Disease Risk Factor Study) was used to compare the designed variable selection method based on an evolutionary search with conventional stepwise selection. The sample contains in total 433 predictor variables and a response variable indicating incidents of cardiovascular diseases for 1465 study subjects.ResultsThe effectiveness of variable selection methods was investigated in combination with two models: Generalized Linear Logistic Regression and Support Vector Machine. We managed to decrease the number of variables from 433 to 38 and save the predictive ability of the models used. Their performance was evaluated with an F-score metric. At most, we gained 65.6% and 67.4% of the F-score before and after variable selection respectively. All the results were averaged over 5-folds of a cross-validation procedure.ConclusionsThe presented evolutionary variable selection method allows a reduced set of variables to be chosen which are relevant to predicting cardiovascular diseases. A reference list of the most meaningful variables is introduced to be used as a basis for new epidemiological studies. In general, the multicollinearity of variables enables different combinations of predictors to be used and the same performance of models to be attained.

In low-dimensional data and within the framework of a classical linear regression model, we intend to compare variable selection methods and investigate the role of shrinkage of regression estimates in a simulation study. Our primary aim is to build descriptive models that capture the data structure parsimoniously, while our secondary aim is to derive a prediction model. Simulation studies are an important tool in statistical methodology research if they are well designed, executed, and reported. However, bias in favor of an “own” preferred method is prevalent in most simulation studies in which a new method is proposed and compared with existing methods. To overcome such bias, neutral comparison studies, which disregard the superiority or inferiority of a particular method, have been proposed. In this paper, we designed a simulation study with key principles of neutral comparison studies in mind, though certain unintentional biases cannot be ruled out. To improve the design and reporting of a simulation study, we followed the recently proposed ADEMP structure, which entails defining the aims (A), data-generating mechanisms (D), estimand/target of analysis (E), methods (M), and performance measures (P). To ensure the reproducibility of results, we published the protocol before conducting the study. In addition, we presented earlier versions of the design to several experts whose feedback influenced certain aspects of the design. We will compare popular penalized regression methods (lasso, adaptive lasso, relaxed lasso, and nonnegative garrote) that combine variable selection and shrinkage with classical variable selection methods (best subset selection and backward elimination) with and without post-estimation shrinkage of parameter estimates.

Interpretability of selected variables and performance comparison of variable selection methods in a polyethylene and polypropylene NIR classification task

Variable selection is important in QSAR modeling since it can improve model performance and transparency, as well as reduce the computational cost of model fitting and predictions. Which variable selection methods that perform well in QSAR settings is largely unknown. To address this question we, in a total of 1728 benchmarking experiments, rigorously investigated how eight variable selection methods affect the predictive performance and transparency of random forest models fitted to seven QSAR datasets covering different endpoints, descriptors sets, types of response variables, and number of chemical compounds. The results show that univariate variable selection methods are suboptimal and that the number of variables in the benchmarked datasets can be reduced with about 60 % without significant loss in model performance when using multivariate adaptive regression splines MARS and forward selection.

The vine weevil (Otiorhynchus sulcatus) is a polyphagous pest that affects various economically important crops, but its potential distribution has not been studied. This research developed multiple species distribution models (SDMs) using different variable selection methods, including correlation, biological considerations, and principal component analysis, and integrated them into an ensemble model to predict the pest’s distribution under climate change. The MaxEnt algorithm was used to develop the models, showing robust performance with raw bioclimatic variables (TSS 0.34–0.37, F1 score 0.60–0.67), while lower performance and different distribution patterns were observed with reconstructed variables (TSS 0.13, F1 score 0.48). The vine weevil was predicted to be primarily distributed in North America and Europe, with the highest invasion risk in Far East Asia and northern India. Climate change could shift its habitat northward, particularly in areas where it currently occurs, and human activities may help spread the pest to new regions. This study offers a potential distribution map to aid in monitoring and controlling the vine weevil, emphasizing the importance of variable selection methods in predictive modeling.

Variable selection methods play an important role in the field of attribute mining. The Naive Bayes (NB) classifier is a very simple and popular classification method that yields good results in a short processing time. Hence, it is a very appropriate classifier for very large datasets. The method has a high dependence on the relationships between the variables. The Info-Gain (IG) measure, which is based on general entropy, can be used as a quick variable selection method. This measure ranks the importance of the attribute variables on a variable under study via the information obtained from a dataset. The main drawback is that it is always non-negative and it requires setting the information threshold to select the set of most important variables for each dataset. We introduce here a new quick variable selection method that generalizes the method based on the Info-Gain measure. It uses imprecise probabilities and the maximum entropy measure to select the most informative variables without setting a threshold. This new variable selection method, combined with the Naive Bayes classifier, improves the original method and provides a valuable tool for handling datasets with a very large number of features and a huge amount of data, where more complex methods are not computationally feasible.