A-DVM: A Self-Adaptive Variable Matrix Decision Variable Selection Scheme for Multimodal Problems
نویسندگان
چکیده
منابع مشابه
Individual adaptation: an adaptive MCMC scheme for variable selection problems
The increasing size of data sets has lead to variable selection in regression becoming increasingly important. Bayesian approaches are attractive since they allow uncertainty about the choice of variables to be formally included in the analysis. The application of fully Bayesian variable selection methods to large data sets is computationally challenging. We describe an adaptive Markov chain Mo...
متن کاملA Particle Swarm Optimization Algorithm for Mixed-Variable Nonlinear Problems
Many engineering design problems involve a combination of both continuous anddiscrete variables. However, the number of studies scarcely exceeds a few on mixed-variableproblems. In this research Particle Swarm Optimization (PSO) algorithm is employed to solve mixedvariablenonlinear problems. PSO is an efficient method of dealing with nonlinear and non-convexoptimization problems. In this paper,...
متن کاملAdaptive Robust Variable Selection.
Heavy-tailed high-dimensional data are commonly encountered in various scientific fields and pose great challenges to modern statistical analysis. A natural procedure to address this problem is to use penalized quantile regression with weighted L1-penalty, called weighted robust Lasso (WR-Lasso), in which weights are introduced to ameliorate the bias problem induced by the L1-penalty. In the ul...
متن کاملVariable selection for optimal treatment decision.
In decision-making on optimal treatment strategies, it is of great importance to identify variables that are involved in the decision rule, i.e. those interacting with the treatment. Effective variable selection helps to improve the prediction accuracy and enhance the interpretability of the decision rule. We propose a new penalized regression framework which can simultaneously estimate the opt...
متن کاملVariable Selection for Optimal Decision Making
This paper discusses variable selection for medical decision making; in particular decisions regarding when to provide treatment and which treatment to provide. Current variable selection techniques were developed for use in a supervised learning setting where the goal is optimal prediction of treatment response. These techniques often leave behind small but important interaction variables that...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Entropy
سال: 2020
ISSN: 1099-4300
DOI: 10.3390/e22091004