Conditional Variable Importance for Random Forests [PDF]
, 2008 Random forests are becoming increasingly popular in many scientific fields because they can cope with ``small n large p'' problems, complex interactions and even highly correlated predictor variables. Their variable importance measures have recently been
Augustin Thomas +14 more
core +1 more source
Variable Selection and Model Choice in Structured Survival Models [PDF]
, 2008 In many situations, medical applications ask for flexible survival models that allow to extend the classical Cox-model via the inclusion of time-varying and nonparametric effects.
Hothorn, Torsten +2 more
core +1 more source
Joint Variable Selection and Classification with Immunohistochemical Data
Biomarker Insights, 2009 To determine if candidate cancer biomarkers have utility in a clinical setting, validation using immunohistochemical methods is typically done. Most analyses of such data have not incorporated the multivariate nature of the staining profiles.
Debashis Ghosh, Ratna Chakrabarti
doaj
Characteristic wavelength selection of volatile organic compounds infrared spectra based on improved interval partial least squares [PDF]
Journal of Innovative Optical Health Sciences, 2019 As important components of air pollutant, volatile organic compounds (VOCs) can cause great harm to environment and human body. The concentration change of VOCs should be focused on in real-time environment monitoring system.
Wei Ju +6 more
doaj +1 more source
Unbiased split selection for classification trees based on the Gini Index [PDF]
, 2005 The Gini gain is one of the most common variable selection criteria in machine learning. We derive the exact distribution of the maximally selected Gini gain in the context of binary classification using continuous predictors by means of a combinatorial ...
Carolin Strobla +8 more
core +1 more source
SNP variable selection by generalized graph domination.
PLoS ONE, 2019 BACKGROUND:High-throughput sequencing technology has revolutionized both medical and biological research by generating exceedingly large numbers of genetic variants.
Shuzhen Sun +7 more
doaj +1 more source
Prior elicitation and variable selection for bayesian quantile regression [PDF]
, 2013 This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Bayesian subset selection suffers from three important difficulties: assigning priors over model space, assigning priors to all components of the regression
Al-Hamzawi, Rahim Jabbar Thaher
core
Bidirectional branch and bound for controlled variable selection. Part II: exact local method for self-optimizing control [PDF]
, 2009 The selection of controlled variables (CVs) from available measurements through enumeration of all possible alternatives is computationally forbidding for large-dimensional problems. In Part I of this work [Cao, Y., & Kariwala, V.
Vinay Cao +5 more
core +1 more source
Fast Algorithm for Impact Point Selection in Semiparametric Functional Models
Proceedings, 2019 A new sparse semiparametric functional model is proposed, which tries to incorporate the influence of two functional variables in a scalar response in a quite simple and interpretable way. One of the functional variables is included trough a single-index
Silvia Novo +2 more
doaj +1 more source
Structured nonlinear variable selection [PDF]
CoRR, 2018 We investigate structured sparsity methods for variable selection in regression problems where the target depends nonlinearly on the inputs. We focus on general nonlinear functions not limiting a priori the function space to additive models. We propose two new regularizers based on partial derivatives as nonlinear equivalents of group lasso and elastic
Magda Gregorova +2 more
openaire +4 more sources