Hybrid mean impact value with nonnegative garrote for input variable selection of multi-layer perceptron

2020 ◽  
Author(s):  
Hongxun Wang ◽  
Fengying Ma ◽  
Kai Sun
Keyword(s):  
Variable Selection ◽  
Multi Layer Perceptron ◽  
Nonnegative Garrote ◽  
Selection Of

Stable variable selection of class-imbalanced data with precision-recall criterion

2017 ◽  
Vol 171 ◽  
pp. 241-250 ◽  
Author(s):  
Guang-Hui Fu ◽  
Feng Xu ◽  
Bing-Yang Zhang ◽  
Lun-Zhao Yi
Keyword(s):  
Variable Selection ◽  
Imbalanced Data ◽  
Stable Variable ◽  
Selection Of

Fingerprint Cryptosystem Using Variable Selection of Minutiae Points

2020 ◽  
pp. 359-369
Author(s):  
Mulagala Sandhya ◽  
Mulagala Dileep ◽  
Akurathi Narayana Murthy ◽  
Md. Misbahuddin
Keyword(s):  
Variable Selection ◽  
Selection Of

Soft-sensor development with adaptive variable selection using nonnegative garrote

2013 ◽  
Vol 21 (9) ◽  
pp. 1157-1164 ◽  
Author(s):  
Jian-Guo Wang ◽  
Shi-Shang Jang ◽  
David Shan-Hill Wong ◽  
Shyan-Shu Shieh ◽  
Chan-Wei Wu
Keyword(s):  
Variable Selection ◽  
Soft Sensor ◽  
Sensor Development ◽  
Nonnegative Garrote

scholarly journals Genetic analysis of complex traits via Bayesian variable selection: the utility of a mixture of uniform priors

2011 ◽  
Vol 93 (4) ◽  
pp. 303-318 ◽  
Author(s):  
TIMO KNÜRR ◽  
ESA LÄÄRÄ ◽  
MIKKO J. SILLANPÄÄ
Keyword(s):  
Genetic Analysis ◽  
Variable Selection ◽  
Complex Traits ◽  
Bayesian Variable Selection ◽  
Stochastic Search Variable Selection ◽  
Indicator Variables ◽  
Supplementary Material ◽  
Selection Of ◽  
Made In ◽  
Search Variable

SummaryA new estimation-based Bayesian variable selection approach is presented for genetic analysis of complex traits based on linear or logistic regression. By assigning a mixture of uniform priors (MU) to genetic effects, the approach provides an intuitive way of specifying hyperparameters controlling the selection of multiple influential loci. It aims at avoiding the difficulty of interpreting assumptions made in the specifications of priors. The method is compared in two real datasets with two other approaches, stochastic search variable selection (SSVS) and a re-formulation of Bayes B utilizing indicator variables and adaptive Student's t-distributions (IAt). The Markov Chain Monte Carlo (MCMC) sampling performance of the three methods is evaluated using the publicly available software OpenBUGS (model scripts are provided in the Supplementary material). The sensitivity of MU to the specification of hyperparameters is assessed in one of the data examples.

Sign in / Sign up

Export Citation Format

Share Document