Results 41 to 50 of about 3,737,276 (277)

Latent class analysis variable selection [PDF]

, 2009
We propose a method for selecting variables in latent class analysis, which is the most common model-based clustering method for discrete data. The method assesses a variable's usefulness for clustering by comparing two models, given the clustering ...
A.E. Raftery, A.L. McCutcheon, Adrian E. Raftery, C. Fraley, C. Keribin, C.C. Clogg, C.C. Clogg, D. Rusakov, G. Galimberti, G.J. McLachlan, J.A. Hagenaars, J.H. Gennari, L. Hubert, L.A. Goodman, Nema Dean, P.F. Lazarsfeld, R. Detrano, R.E. Kass, The International HapMap Consortium, W.M. Rand   +19 more
core   +4 more sources

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

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

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

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

Variable Selection and Parameter Tuning in High-Dimensional Prediction [PDF]

, 2010
In the context of classification using high-dimensional data such as microarray gene expression data, it is often useful to perform preliminary variable selection.
Bernau, Christoph, Boulesteix, Anne-Laure   +1 more
core   +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

Variable Selection in Multivariable Regression Using SAS/IML

Journal of Statistical Software, 2002
This paper introduces a SAS/IML program to select among the multivariate model candidates based on a few well-known multivariate model selection criteria. Stepwise regression and all-possible-regression are considered.
Ali A. Al-Subaihi
doaj   +3 more sources

Statistical Sources of Variable Selection Bias in Classification Tree Algorithms Based on the Gini Index [PDF]

, 2005
Evidence for variable selection bias in classification tree algorithms based on the Gini Index is reviewed from the literature and embedded into a broader explanatory scheme: Variable selection bias in classification tree algorithms based on the Gini ...
Strobl, Carolin
core   +2 more sources

Variable selection in semiparametric regression modeling

, 2008
In this paper, we are concerned with how to select significant variables in semiparametric modeling. Variable selection for semiparametric regression models consists of two components: model selection for nonparametric components and selection of ...
Li, Runze, Liang, Hua
core   +2 more sources