ga_parsimony-class {GAparsimony} | R Documentation |
Class "ga_parsimony"
Description
An S4 class for searching parsimonious models by feature selection and parameter tuning with genetic algorithms.
Objects from the Class
Objects can be created by calls to the ga_parsimony
function.
Slots
call
an object of class
"call"
representing the matched call;min_param
a vector of length equal to the model parameters providing the minimum of the search space;
max_param
a vector of length equal to the model parameters providing the maximum of the search space;
nParams
a value specifying the number of model parameter to be tuned;
feat_thres
proportion of selected features in the initial population. It is recommended a high percentage of selected features for the first generations;
feat_mut_thres
threshold to consider a random number between 0 and 1 is considered one if a value of the parameters-chromosome is muted. Default value is set to 0.5;
not_muted
number of the best elitists that are not muted. Default value is set to 3;
rerank_error
when a value distinct to zero is provided a second reranking process according to the model complexities is called by 'parsimonyReRank' function. Its primary objective is to select individuals with high validation cost while maintaining the robustness of a parsimonious model. This function switches the position of two models if the first one is more complex than the latter and no significant difference is found between their fitness values in terms of cost. Therefore, if the absolute difference between the validation costs are lower than 'rerank_error' they are considered similar. Default value=0.01;
nFeatures
a value specifying the number of maximum input features;
names_param
a vector with the name of the model parameters;
names_features
a vector with the name of the input features;
popSize
the population size;
iter
the actual (or final) iteration of GA search;
iter_start_rerank
iteration when ReRanking process is actived. Default=0. Sometimes is useful not to use ReRanking process in the first generations;
early_stop
the number of consecutive generations without any improvement in the best fitness value before the GA is stopped;
maxiter
the maximum number of iterations to run before the GA search is halted;
minutes_gen
elapsed time of this generation (in minutes);
minutes_total
total elapsed time (in minutes);
suggestions
a matrix of user provided solutions and included in the initial population;
population
the current (or final) population;
elitism
the number of best fitness individuals to survive at each generation;
pcrossover
the crossover probability;
pmutation
the mutation probability;
best_score
the best validation score in the whole GA process;
solution_best_score
Solution with the best validation score in the whole GA process;
fitnessval
the values of validation cost for the current (or final) population;
fitnesstst
the values of testing cost for the current (or final) population;
complexity
the values of model complexities for the current (or final) population;
summary
a matrix of summary statistics for fitness values at each iteration (along the rows);
bestSolList
a list with the best solution of all iterations;
bestfitnessVal
the validation cost of the best solution at the last iteration;
bestfitnessTst
the testing cost of the best solution at the last iteration;
bestcomplexity
the model complexity of the best solution at the last iteration;
bestsolution
the best solution at the last iteration;
history
a list with the population of all iterations;
Author(s)
Francisco Javier Martinez-de-Pison. fjmartin@unirioja.es. EDMANS Group. http://www.mineriadatos.com
See Also
For examples of usage see ga_parsimony
.