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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
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Variable Priority for Unsupervised Variable Selection. [PDF]
Pattern RecognitIn 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.
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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
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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 +2 more
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Variable Selection in ROC Regression [PDF]
Computational and Mathematical Methods in Medicine, 2013 Regression models are introduced into thereceiver operating characteristic(ROC) analysis to accommodate effects of covariates, such as genes. If many covariates are available, the variable selection issue arises. The traditional induced methodology separately models outcomes of diseased and nondiseased groups; thus, separate application of variable ...
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Stability Selection for Structured Variable Selection
CoRR, 2017 In variable or graph selection problems, finding a right-sized model or controlling the number of false positives is notoriously difficult. Recently, a meta-algorithm called Stability Selection was proposed that can provide reliable finite-sample control of the number of false positives.
George Philipp +2 more
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A-DVM: A Self-Adaptive Variable Matrix Decision Variable Selection Scheme for Multimodal Problems
Entropy, 2020 Artificial Bee Colony (ABC) is a Swarm Intelligence optimization algorithm well known for its versatility. The selection of decision variables to update is purely stochastic, incurring several issues to the local search capability of the ABC.
Marco Antonio Florenzano Mollinetti +3 more
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Application of variable selection and dimension reduction on predictors of MSE’s development
Journal of Big Data, 2019 Nature create variables using its character component, and variables are sharing characters from a vary small to relatively large scale. This results, variables to have from a vary different to a more similar character, and leads to have a relation ship.
Habtamu Tilaye Wubetie
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Variable selection in multivariate multiple regression.
PLoS ONE, 2020 IntroductionIn many practical situations, we are interested in the effect of covariates on correlated multiple responses. In this paper, we focus on estimation and variable selection in multi-response multiple regression models.
Asokan Mulayath Variyath, Anita Brobbey
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Identifying Plant Pentatricopeptide Repeat Proteins Using a Variable Selection Method
Frontiers in Plant Science, 2021 Motivation: Pentatricopeptide repeat (PPR), which is a triangular pentapeptide repeat domain, plays an important role in plant growth. Features extracted from sequences are applicable to PPR protein identification using certain classification methods ...
Xudong Zhao +5 more
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