Description Usage Arguments Author(s) References Examples
Make a subgroup discovery task using the FuGePSD algorithm.
1 2 3 4 5 6 7 8  FUGEPSD(paramFile = NULL, training = NULL, test = NULL,
output = c("optionsFile.txt", "rulesFile.txt", "testQM.txt"), seed = 0,
nLabels = 3, t_norm = "product_tnorm", ruleWeight = "Certainty_Factor",
frm = "Normalized_Sum", numGenerations = 300,
numberOfInitialRules = 100, crossProb = 0.5, mutProb = 0.2,
insProb = 0.15, dropProb = 0.15, tournamentSize = 2,
globalFitnessWeights = c(0.7, 0.1, 0.05, 0.2), minCnf = 0.6,
ALL_CLASS = TRUE, targetVariable = NA)

paramFile 
The path of the parameters file. 
training 
A 
test 
A 
output 
Character vector with the paths where store information file, rules file and test quality measures file, respectively. For rules and quality measures files, the algorithm generate 4 files, each one with the results of a given filter of fuzzy confidence. 
seed 
An integer to set the seed used for generate random numbers. 
nLabels 
Number of linguistic labels for numerical variables. By default 3. We recommend an odd number between 3 and 9. 
t_norm 
A string with the tnorm to use when computing the compatibilty degree of the rules. Use 
ruleWeight 
String with the method to calculate the rule weight. Possible values are:

frm 
A string specifying the Fuzzy Reasoning Method to use. Possible Values are:

numGenerations 
An integer to set the number of generations to perfom before stop the evolutionary process. 
numberOfInitialRules 
An integer to set the number individuals or rules in the initial population. 
crossProb 
Sets the crossover probability. We recommend a number in [0,1]. 
mutProb 
Sets the mutation probability. We recommend a number in [0,1]. 
insProb 
Sets the insertion probability. We recommend a number in [0,1]. 
dropProb 
Sets the dropping probability. We recommend a number in [0,1]. 
tournamentSize 
Sets the number of individuals that will be chosen in the tournament selection procedure. This number must be greater than or equal to 2. 
globalFitnessWeights 
A numeric vector of length 4 specifying the weights used in the computation of the Global Fitness Parameter. 
minCnf 
A value in [0,1] to filter rules with a minimum confidence 
ALL_CLASS 
if TRUE, the algorithm returns, at least, the best rule for each target class, even if it does not pass the filters. If FALSE, it only returns, at least, the best rule if there are not rules that passes the filters. 
targetVariable 
The name or index position of the target variable (or class). It must be a categorical one. @details This function sets as target variable the last one that appear in If you specify in @return The algorithm shows in console the following results:
Also, this results are saved in a file with rules and other with the quality measures, one file per filter. @section How does this algorithm work?: This algorithm performs a EFS based on a genetic programming algorithm. This algorithm starts with an initial population generated in a random manner where individuals are represented through the "chromosome = individual" approach includind both antecedent and consequent of the rule. The representation of the consequent has the advantage of getting rules for all target class with only one execution of the algorithm. The algorithm employs a cooperativecompetition approach were rules of the population cooperate and compete between them in order to obtain the optimal solution. So this algorithm performs to evaluation, one for individual rules to competition and other for the total population for cooperation. The algorithm evolves generating an offspring population of the same size than initial generated by the application of the genetic operators over the main population. Once applied, both populations are joined a token competition is performed in order to mantain the diversity of the rules generated. Also, this token competition reduce the population sice deleting those rules that are not competitive. After the evolutionary process a screening function is applied over the best population. This screening function filter the rules that have a minimium level of confidence and sensitivity. Those levels are 0.6 for sensitivy and four filters of 0.6, 0.7, 0.8 and 0.9 for fuzzy confidence are performed. Also, the user can force the algorithm return at least one rule for all target class values, even if not pass the screening function. This behaviour is specified by the ALL_CLASS parameter. @section Parameters file structure:
The
An example of parameter file could be: algorithm = FUGEPSD inputData = "banana51tra.dat" "banana51tst.dat" outputData = "Parameters_INFO.txt" "Rules.txt" "TestMeasures.txt" seed = 23783 Number of Labels = 3 Tnorm/Tconorm for the Computation of the Compatibility Degree = Normalized_Sum Rule Weight = Certainty_Factor Fuzzy Reasoning Method = Normalized_Sum Number of Generations = 300 Initial Number of Fuzzy Rules = 100 Crossover probability = 0.5 Mutation probability = 0.2 Insertion probability = 0.15 Dropping Condition probability = 0.15 Tournament Selection Size = 2 Global Fitness Weight 1 = 0.7 Global Fitness Weight 2 = 0.1 Global Fitness Weight 3 = 0.05 Global Fitness Weight 4 = 0.2 All Class = true 
Written on R by Angel M. Garcia <[email protected]>
A fuzzy genetic programmingbased algorithm for subgroup discovery and the application to one problem of pathogenesis of acute sore throat conditions in humans, Carmona, C.J., RuizRodado V., del Jesus M.J., Weber A., Grootveld M., Gonzalez P., and Elizondo D. , Information Sciences, Volume 298, p.180197, (2015)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  FUGEPSD(training = habermanTra,
test = habermanTst,
output = c("parametersFile.txt", "rulesFile.txt", "testQM.txt"),
seed = 23783,
nLabels = 3,
t_norm = "Minimum/Maximum",
ruleWeight = "Certainty_Factor",
frm = "Normalized_Sum",
numGenerations = 20,
numberOfInitialRules = 15,
crossProb = 0.5,
mutProb = 0.2,
insProb = 0.15,
dropProb = 0.15,
tournamentSize = 2,
globalFitnessWeights = c(0.7, 0.1, 0.3, 0.2),
ALL_CLASS = TRUE)
## Not run:
Execution with a parameters file called 'ParamFile.txt' in the working directory:
FUGEPSD("ParamFile.txt")
## End(Not run)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.