A variable selection method based on Tabu search for logistic regression models

DOI10.1016/j.ejor.2008.10.007zbMath1176.90268OpenAlexW1996777278MaRDI QIDQ1042170

Joaquín Pacheco, Silvia Casado, Laura Núñez

Publication date: 7 December 2009

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2008.10.007

zbMATH Keywords

metaheuristicsTabu searchlogistic regressionvariable selection

Mathematics Subject Classification ID

Search theory (90B40)

Related Items (14)

Exact methods for variable selection in principal component analysis: guide functions and pre-selection ⋮ A comparison of simulated annealing algorithms for variable selection in principal component analysis and discriminant analysis ⋮ Feature subset selection for logistic regression via mixed integer optimization ⋮ An approach for knowledge acquisition from a survey data by conducting Bayesian network modeling, adopting the robust coplot method ⋮ Feature selection for support vector machines using generalized Benders decomposition ⋮ Particle swarm optimization-based variable selection in Poisson regression analysis via information complexity-type criteria ⋮ Variable selection in linear regression analysis with alternative Bayesian information criteria using differential evaluation algorithm ⋮ Income prediction in the agrarian sector using product unit neural networks ⋮ Bayesian variable selection in generalized linear models using a combination of stochastic optimization methods ⋮ Exact and approximate algorithms for variable selection in linear discriminant analysis ⋮ Variable selection in gamma regression models via artificial bee colony algorithm ⋮ Rough support vector regression ⋮ A discrete particle swarm optimization method for feature selection in binary classification problems ⋮ Memory and Learning in Metaheuristics

Uses Software

• UCI-ml
• SPSS
• Tabu search
• BMDP

Cites Work

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• Using simulated annealing to optimize the feature selection problem in marketing applications
• Automatic digital modulation recognition using artificial neural network and genetic algorithm
• Analysis of new variable selection methods for discriminant analysis
• Feature Subset Selection by Bayesian network-based optimization
• Efficient strategies for deriving the subset VAR models
• Solving feature subset selection problem by a parallel scatter search
• Tabu Search—Part I
• Tabu Search—Part II
• A Branch and Bound Algorithm for Feature Subset Selection
• Ridge Estimators in Logistic Regression
• Feature subset selection by Bayesian networks: A comparison with genetic and sequential algorithms

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