scholarly journals An Outlier Ranking Tree Selection Approach to Extreme Pruning of Random Forests

2016 ◽  
pp. 267-282
Author(s):  
Khaled Fawagreh ◽  
Mohamed Medhat Gaber ◽  
Eyad Elyan
Keyword(s):  
Random Forests ◽  
Selection Approach

scholarly journals Variable selection with missing data in both covariates and outcomes: Imputation and machine learning

2021 ◽  
pp. 096228022110463
Author(s):  
Liangyuan Hu ◽  
Jung-Yi Joyce Lin ◽  
Jiayi Ji
Keyword(s):  
Machine Learning ◽  
Missing Data ◽  
Variable Selection ◽  
Random Forests ◽  
Gradient Boosting ◽  
Stepwise Selection ◽  
Machine Learning Methods ◽  
Extreme Gradient Boosting ◽  
Selection Approach ◽  
Additive Regression

Variable selection in the presence of both missing covariates and outcomes is an important statistical research topic. Parametric regression are susceptible to misspecification, and as a result are sub-optimal for variable selection. Flexible machine learning methods mitigate the reliance on the parametric assumptions, but do not provide as naturally defined variable importance measure as the covariate effect native to parametric models. We investigate a general variable selection approach when both the covariates and outcomes can be missing at random and have general missing data patterns. This approach exploits the flexibility of machine learning models and bootstrap imputation, which is amenable to nonparametric methods in which the covariate effects are not directly available. We conduct expansive simulations investigating the practical operating characteristics of the proposed variable selection approach, when combined with four tree-based machine learning methods, extreme gradient boosting, random forests, Bayesian additive regression trees, and conditional random forests, and two commonly used parametric methods, lasso and backward stepwise selection. Numeric results suggest that, extreme gradient boosting and Bayesian additive regression trees have the overall best variable selection performance with respect to the [Formula: see text] score and Type I error, while the lasso and backward stepwise selection have subpar performance across various settings. There is no significant difference in the variable selection performance due to imputation methods. We further demonstrate the methods via a case study of risk factors for 3-year incidence of metabolic syndrome with data from the Study of Women’s Health Across the Nation.

ON SUITABLE MODEL SELECTION APPROACH FOR ANALYSIS OF MENTAL HEALTH SERVICE USE DATA

2019 ◽  
Vol 43 (2) ◽  
pp. 212-222
Author(s):  
DULAL K. BHAUMIK ◽  
◽  
SUBHASH ARYAL ◽  
SCOTT HUR ◽  
◽  
...  
Keyword(s):  
Mental Health ◽  
Mental Health Service ◽  
Model Selection ◽  
Health Service ◽  
Service Use ◽  
Mental Health Service Use ◽  
Health Service Use ◽  
Suitable Model ◽  
Model Selection Approach ◽  
Selection Approach

A throughput-aware joint vehicle route and access network selection approach based on SMDP

2020 ◽  
Vol 17 (5) ◽  
pp. 243-265 ◽  
Author(s):  
Jiandong Xie ◽  
Sa Xiao ◽  
Ying-Chang Liang ◽  
Li Wang ◽  
Jun Fang
Keyword(s):  
Access Network ◽  
Network Selection ◽  
Vehicle Route ◽  
Selection Approach ◽  
Access Network Selection

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

Improving Practices for Selecting a Subset of Important Predictors in Psychology: An Application to Predicting Pain

2019 ◽  
Author(s):  
Sierra Bainter ◽  
Thomas Granville McCauley ◽  
Tor D Wager ◽  
Elizabeth Reynolds Losin
Keyword(s):  
Variable Selection ◽  
Bayesian Variable Selection ◽  
Limited Information ◽  
Online Application ◽  
Stochastic Search Variable Selection ◽  
Selection Approach ◽  
Pain Ratings ◽  
Research Questions ◽  
Standard Techniques ◽  
Search Variable

In this paper we address the problem of selecting important predictors from some larger set of candidate predictors. Standard techniques are limited by lack of power and high false positive rates. A Bayesian variable selection approach used widely in biostatistics, stochastic search variable selection, can be used instead to combat these issues by accounting for uncertainty in the other predictors of the model. In this paper we present Bayesian variable selection to aid researchers facing this common scenario, along with an online application (https://ssvsforpsych.shinyapps.io/ssvsforpsych/) to perform the analysis and visualize the results. Using an application to predict pain ratings, we demonstrate how this approach quickly identifies reliable predictors, even when the set of possible predictors is larger than the sample size. This technique is widely applicable to research questions that may be relatively data-rich, but with limited information or theory to guide variable selection.

Research on Service Selection Approach Based on Composite Service Execution Information

2009 ◽  
Vol 31 (8) ◽  
pp. 1398-1411 ◽  
Author(s):  
Ming-Wei ZHANG ◽  
Wei-Jie WEI ◽  
Bin ZHANG ◽  
Xi-Zhe ZHANG ◽  
Zhi-Liang ZHU
Keyword(s):  
Service Selection ◽  
Composite Service ◽  
Service Execution ◽  
Selection Approach
Sign in / Sign up

Export Citation Format

Share Document