Variable subset selection Variables

Generating Optimal Linear PLS Estimations = GOLPE variable selection > genetic algorithm - variable subset selection variable selection... 

Within the framework of the variable subset selection techniques, the general linear regression model can be expressed as ... 

The methods for variable subset selection can be divided into two main categories methods which work on the original p variables xi, X2,. .., Xp, direct variable subset selection, where the best binary vector I is evaluated by considering the relationships among the x, variables and a response variable y, i.e. 

Within the framework of the variable subset selection techniques, a vector T is usually defined, which collects binary variables indicating the presence of the jth variable (IJ = 1) or its absence (It = 0) in the final model. This p-dimensional vector T is usually obtained by validation. The actual model dimension k (k[Pg.848]

The methods for variable subset selection can be divided into two main categories methods... 

This procedure can be used to perform a preliminary screening of the variables, while other techniques are used on the remaining variables, that is, genetic algorithm variable subset selection or searching for all subset models. A disadvantage of this procedure is that a variable with a low correlation with the response, but correlated with residuals and thus able to give a contribution in the final model, is excluded. 

Pavan, M., Consonni, V. and Todeschini, R. (2005) Partial ranking models by genetic algorithms variable subset selection (GA-VSS) approach for environmental priority settings. MATCH Commun. Math. Comput. Chem., 54, 583-609. 

Todeschini, R., Consonni, V., Mauri, A. and Pavan, M. (2004b) New fitness frmctions to avoid bad regression models in variable subset selection by genetic algorithms, in Designing Drugs and Crop Protectants Processes, Problems and Solutions (eds M. Ford, D.J. Livingstone, J.C. Dearden and H. van de Waterbeemd), Blackwell, Oxford, UK, pp. 323-325. 

New QSAR Modelling Approach Based on Ranking Models by Genetic Algorithms - Variable Subset Selection (GA-VSS)... 

Variable subset selection is performed by GAs, optimising populations of models according to a defined objective function related to model quality. In partial ranking models objective function is an expression of the degree of agreement between the element ranking resulting from experimental attributes and that provided by the selected subset of model attributes. 

Table 4 Values of the Upper 5% Points of the Distribution of for Variable Subsets Selected Using the Stepwise Regression Algorithm of Efroymson ...

LIBS is a suitable method for on-line analysis of the elemental composition of recycled complex ther-moplasts from consumer electronics (ABS, PA, PC, PS, SBR, PPG, TPO, PVC, PPO/PS). The process analysis of such thermoplasts by LIBS (Nd YAG at 266 nm) was reported [139] data analysis consisted of multivariate methods and variable subset selection via a genetic algorithm. Reference analysis was... 

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