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

2009 ◽  
Vol 199 (2) ◽  
pp. 506-511 ◽  
Author(s):  
Joaquín Pacheco ◽  
Silvia Casado ◽  
Laura Núñez
Keyword(s):  
Logistic Regression ◽  
Tabu Search ◽  
Variable Selection ◽  
Regression Models ◽  
Selection Method ◽  
Logistic Regression Models ◽  
Variable Selection Method

Variable Selection in Logistic Regression Models

2004 ◽  
Vol 33 (3) ◽  
pp. 787-805 ◽  
Author(s):  
Dietmar Zellner ◽  
Frieder Keller ◽  
Günter E. Zellner
Keyword(s):  
Logistic Regression ◽  
Variable Selection ◽  
Regression Models ◽  
Logistic Regression Models

Variable Selection Method for Regression Models Using Computational Intelligence Techniques

2021 ◽  
pp. 742-761
Author(s):  
Dhamodharavadhani S. ◽  
Rathipriya R.
Keyword(s):  
Variable Selection ◽  
Regression Model ◽  
Computational Intelligence ◽  
Regression Models ◽  
Selection Method ◽  
Predictor Variables ◽  
Filter Method ◽  
Selection Methods ◽  
Forward Selection ◽  
Variable Selection Method

Regression model (RM) is an important tool for modeling and analyzing data. It is one of the popular predictive modeling techniques which explore the relationship between a dependent (target) and independent (predictor) variables. The variable selection method is used to form a good and effective regression model. Many variable selection methods existing for regression model such as filter method, wrapper method, embedded methods, forward selection method, Backward Elimination methods, stepwise methods, and so on. In this chapter, computational intelligence-based variable selection method is discussed with respect to the regression model in cybersecurity. Generally, these regression models depend on the set of (predictor) variables. Therefore, variable selection methods are used to select the best subset of predictors from the entire set of variables. Genetic algorithm-based quick-reduct method is proposed to extract optimal predictor subset from the given data to form an optimal regression model.

Variable Selection Method for Regression Models Using Computational Intelligence Techniques

2020 ◽  
pp. 416-436 ◽  
Author(s):  
Dhamodharavadhani S. ◽  
Rathipriya R.
Keyword(s):  
Variable Selection ◽  
Regression Model ◽  
Computational Intelligence ◽  
Regression Models ◽  
Selection Method ◽  
Predictor Variables ◽  
Filter Method ◽  
Selection Methods ◽  
Forward Selection ◽  
Variable Selection Method

Regression model (RM) is an important tool for modeling and analyzing data. It is one of the popular predictive modeling techniques which explore the relationship between a dependent (target) and independent (predictor) variables. The variable selection method is used to form a good and effective regression model. Many variable selection methods existing for regression model such as filter method, wrapper method, embedded methods, forward selection method, Backward Elimination methods, stepwise methods, and so on. In this chapter, computational intelligence-based variable selection method is discussed with respect to the regression model in cybersecurity. Generally, these regression models depend on the set of (predictor) variables. Therefore, variable selection methods are used to select the best subset of predictors from the entire set of variables. Genetic algorithm-based quick-reduct method is proposed to extract optimal predictor subset from the given data to form an optimal regression model.

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