A Comparative Study of Variable Selection Methods for High Dimensional Data Based on Logistic Regression Model

2019 ◽  
Vol 08 (03) ◽  
pp. 553-559
Author(s):  
丹 廖
Keyword(s):  
Logistic Regression ◽  
Variable Selection ◽  
Comparative Study ◽  
Regression Model ◽  
Logistic Regression Model ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Selection Methods

scholarly journals Cancer classification and biomarker selection via a penalized logsum network-based logistic regression model

2021 ◽  
Vol 29 ◽  
pp. 287-295
Author(s):  
Zhiming Zhou ◽  
Haihui Huang ◽  
Yong Liang
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Gene Selection ◽  
Simulated Data ◽  
Biological Data ◽  
Cancer Classification ◽  
High Dimensional ◽  
Data Set ◽  
Biomarker Selection

BACKGROUND: In genome research, it is particularly important to identify molecular biomarkers or signaling pathways related to phenotypes. Logistic regression model is a powerful discrimination method that can offer a clear statistical explanation and obtain the classification probability of classification label information. However, it is unable to fulfill biomarker selection. OBJECTIVE: The aim of this paper is to give the model efficient gene selection capability. METHODS: In this paper, we propose a new penalized logsum network-based regularization logistic regression model for gene selection and cancer classification. RESULTS: Experimental results on simulated data sets show that our method is effective in the analysis of high-dimensional data. For a large data set, the proposed method has achieved 89.66% (training) and 90.02% (testing) AUC performances, which are, on average, 5.17% (training) and 4.49% (testing) better than mainstream methods. CONCLUSIONS: The proposed method can be considered a promising tool for gene selection and cancer classification of high-dimensional biological data.

Bayesian Variable Selection for Semiparametric Logistic Regression

2021 ◽  
Vol 26 (5) ◽  
pp. 44-57
Author(s):  
Zainab Sami ◽  
Taha Alshaybawee
Keyword(s):  
Logistic Regression ◽  
Variable Selection ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Numerical Data ◽  
Real Data ◽  
Bayesian Variable Selection ◽  
Bayesian Lasso ◽  
Posterior Inference ◽  
Semiparametric Logistic Regression

Lasso variable selection is an attractive approach to improve the prediction accuracy. Bayesian lasso approach is suggested to estimate and select the important variables for single index logistic regression model. Laplace distribution is set as prior to the coefficients vector and prior to the unknown link function (Gaussian process). A hierarchical Bayesian lasso semiparametric logistic regression model is constructed and MCMC algorithm is adopted for posterior inference. To evaluate the performance of the proposed method BSLLR is through comparing it to three existing methods BLR, BPR and BBQR. Simulation examples and numerical data are to be considered. The results indicate that the proposed method get the smallest bias, SD, MSE and MAE in simulation and real data. The proposed method BSLLR performs better than other methods. 

LASSO-Type Variable Selection Methods for High-Dimensional Data

2013 ◽  
Vol 444-445 ◽  
pp. 604-609
Author(s):  
Guang Hui Fu ◽  
Pan Wang
Keyword(s):  
Variable Selection ◽  
High Dimensional Data ◽  
Real Data ◽  
Oracle Property ◽  
High Dimensional ◽  
Data Sets ◽  
Group Effect ◽  
Selection Methods ◽  
Variable Selection Method ◽  
Type Variable

LASSO is a very useful variable selection method for high-dimensional data , But it does not possess oracle property [Fan and Li, 200 and group effect [Zou and Hastie, 200. In this paper, we firstly review four improved LASSO-type methods which satisfy oracle property and (or) group effect, and then give another two new ones called WFEN and WFAEN. The performance on both the simulation and real data sets shows that WFEN and WFAEN are competitive with other LASSO-type methods.

scholarly journals Variable selection in Logistic regression model with genetic algorithm

2018 ◽  
Vol 6 (3) ◽  
pp. 45-45 ◽  
Author(s):  
Zhongheng Zhang ◽  
Victor Trevino ◽  
Sayed Shahabuddin Hoseini ◽  
Smaranda Belciug ◽  
Arumugam Manivanna Boopathi ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Logistic Regression ◽  
Variable Selection ◽  
Regression Model ◽  
Logistic Regression Model
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