Results 31 to 40 of about 3,771,811 (275)

Analysis of Information-Based Nonparametric Variable Selection Criteria

Entropy, 2020
We consider a nonparametric Generative Tree Model and discuss a problem of selecting active predictors for the response in such scenario. We investigated two popular information-based selection criteria: Conditional Infomax Feature Extraction (CIFE) and ...
Małgorzata Łazęcka, Jan Mielniczuk
doaj   +1 more source

Efficient Test-based Variable Selection for High-dimensional Linear Models [PDF]

, 2018
Variable selection plays a fundamental role in high-dimensional data analysis. Various methods have been developed for variable selection in recent years.
Gong, Siliang, Liu, Yufeng, Zhang, Kai
core   +3 more sources

Scalable Importance Tempering and Bayesian Variable Selection [PDF]

, 2019
We propose a Monte Carlo algorithm to sample from high dimensional probability distributions that combines Markov chain Monte Carlo and importance sampling.
Roberts, Gareth, Zanella, Giacomo
core   +2 more sources

Reinforced variable selection

Statistical Theory and Related Fields
Variable selection identifies the best subset of covariates when building the prediction model, among all possible subsets. In this paper, we propose a novel reinforced variable selection method, called ‘Actor-Critic-Predictor’. The actor takes an action
Yuan Le, Yang Bai, Fan Zhou
doaj   +1 more source

Feature Selection based on the Local Lift Dependence Scale

Entropy, 2018
This paper uses a classical approach to feature selection: minimization of a cost function applied on estimated joint distributions. However, in this new formulation, the optimization search space is extended.
Diego Marcondes, Adilson Simonis, Junior Barrera   +2 more
doaj   +1 more source

Extreme Gradient Boosting Combined with Conformal Predictors for Informative Solubility Estimation

Molecules, 2023
We used the extreme gradient boosting (XGB) algorithm to predict the experimental solubility of chemical compounds in water and organic solvents and to select significant molecular descriptors.
Ozren Jovic, Rabah Mouras
doaj   +1 more source

Adaptive robust variable selection

, 2014
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 $L_1 ...
Barut, Emre, Fan, Jianqing, Fan, Yingying   +2 more
core   +1 more source

Applying Variable Selection Methods and Preprocessing Techniques to Hyperspectral Reflectance Data to Estimate Tea Cultivar Chlorophyll Content

Remote Sensing, 2022
Tea is second only to water as the world’s most popular drink and it is consumed in various forms, such as black and green teas. A range of cultivars has therefore been developed in response to customer preferences.
Rei Sonobe, Yuhei Hirono
doaj   +1 more source

Shrinkage and Variable Selection by Polytopes [PDF]

, 2009
Constrained estimators that enforce variable selection and grouping of highly correlated data have been shown to be successful in finding sparse representations and obtaining good performance in prediction.
Petry, Sebastian, Tutz, Gerhard
core   +1 more source

Finite mixture regression: A sparse variable selection by model selection for clustering [PDF]

, 2014
We consider a finite mixture of Gaussian regression model for high- dimensional data, where the number of covariates may be much larger than the sample size. We propose to estimate the unknown conditional mixture density by a maximum likelihood estimator,
Devijver, Emilie
core   +4 more sources