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Variable Priority for Unsupervised Variable Selection. [PDF]

Pattern Recognit
In unsupervised settings where labeled data is unavailable, identifying informative features is both challenging and essential. Although numerous methods for unsupervised feature selection have been proposed, significant opportunities for improvement remain.
Zhou L, Lu M, Ishwaran H.
europepmc   +3 more sources

Variable Selection by Perfect Sampling [PDF]

EURASIP Journal on Advances in Signal Processing, 2002
Variable selection is very important in many fields, and for its resolution many procedures have been proposed and investigated. Among them are Bayesian methods that use Markov chain Monte Carlo (MCMC) sampling algorithms.
Huang Yufei, Djurić Petar M
doaj   +2 more sources

ShadowVIMP: permutation-based multiple testing-controlled variable selection [PDF]

BMC Bioinformatics
Background Identifying relevant biomarkers is critical in clinical research and precision medicine, particularly when analysing high-dimensional data. Random forests (RFs) are promising for such settings due to their flexibility, ease of use, and their ...
Tim Müller, Roman Hornung, Silke Szymczak, Hannes Buchner   +3 more
doaj   +2 more sources

Systematic Review of Variable Selection Bias in Species Distribution Models for Aedes vexans (Diptera: Culicidae) [PDF]

Insects
We conducted a systematic literature review, following PRISMA guidelines, to assess whether existing species distribution models for Aedes vexans reflect its known ecological requirements.
Peter Pothmann, Helge Kampen, Doreen Werner, Hans-Hermann Thulke   +3 more
doaj   +2 more sources

Variable selection methods for descriptive modeling [PDF]

PLoS ONE
A. D. V. Tharkeshi T. Dharmaratne, Alysha De Livera, Stelios Georgiou, Stella Stylianou   +3 more
doaj   +2 more sources

Variable selection for decentralized control [PDF]

Modeling, Identification and Control, 1992
Decentralized controllers (single-loop controllers applied to multivariable plants) are often preferred in practice because they are robust and relatively simple to understand and to change. The design of such a control system starts with pairing inputs (
Sigurd Skogestad, Manfred Morari
doaj   +1 more source

Variable Selection for Clustering and Classification [PDF]

Journal of Classification, 2013
As data sets continue to grow in size and complexity, effective and efficient techniques are needed to target important features in the variable space. Many of the variable selection techniques that are commonly used alongside clustering algorithms are based upon determining the best variable subspace according to model fitting in a stepwise manner ...
Jeffrey L. Andrews, Paul D. McNicholas
openaire   +2 more sources

Gibbs Variable Selection Using BUGS

Journal of Statistical Software, 2002
In this paper we discuss and present in detail the implementation of Gibbs variable selection as defined by Dellaportas et al. (2000, 2002) using the BUGS software (Spiegelhalter et al. , 1996a,b,c).
Ioannis Ntzoufras
doaj   +3 more sources

Variable Selection of Lasso and Large Model

IEEE Access, 2023
In order to clarify the variable selection of Lasso, Lasso is compared with two other variable selection methods AIC and forward stagewise. First, the variable selection of Lasso was compared with that of AIC, and it was discovered that Lasso has a wider
Huiyi Xia
doaj   +1 more source

Variable Selection is Hard

CoRR, 2014
Variable selection for sparse linear regression is the problem of finding, given an m x p matrix B and a target vector y, a sparse vector x such that Bx approximately equals y. Assuming a standard complexity hypothesis, we show that no polynomial-time algorithm can find a k'-sparse x with ||Bx-y||^2<=h(m,p), where k'=k*2^{log^{1-delta} p} and h(m,p)&
Dean P. Foster, Howard J. Karloff, Justin Thaler   +2 more
openaire   +3 more sources