DependencyBasedStrategyConfiguration: Custom Strategy Configuration handler for the...
In D2MCS: Data Driving Multiple Classifier System
DependencyBasedStrategyConfiguration R Documentation
Custom Strategy Configuration handler for the DependencyBasedStrategy
strategy.
Description
Define the default configuration parameters for the
DependencyBasedStrategy strategy.
Super class
D2MCS::StrategyConfiguration -> DependencyBasedStrategyConfiguration
Methods
Public methods
Method new()
Method for initializing the object arguments during runtime.
Usage
DependencyBasedStrategyConfiguration$new(
binaryCutoff = 0.6,
realCutoff = 0.6,
tiebreakMethod = "lfdc",
metric = "dep.tar"
)
Arguments
binaryCutoffThe numeric value of binary cutoff.
realCutoffThe numeric value of real cutoff.
tiebreakMethodThe character value of tie-break method. The
two tiebreak methods available are "lfdc" (less dependence cluster with
the features) and "ltdc" (less dependence cluster with the target). These
methods are used to add the features in the candidate feature clusters.
metricThe character value of the metric to apply the mean
to obtain the quality of a cluster. The two metrics available are
"dep.tar" (Dependence of cluster features on the target) and "dep.fea"
(Dependence between cluster features).
Method minNumClusters()
Function used to return the minimum number of clusters
distributions used. By default the minimum is set in 2.
Usage
DependencyBasedStrategyConfiguration$minNumClusters(...)
Arguments
...Further arguments passed down to minNumClusters
function.
Returns
A numeric vector of length 1.
Method maxNumClusters()
The function is responsible of returning the maximum number
of cluster distributions used. By default the maximum number is set in 50.
Usage
DependencyBasedStrategyConfiguration$maxNumClusters(...)
Arguments
...Further arguments passed down to maxNumClusters
function.
Returns
A numeric vector of length 1.
Method getBinaryCutoff()
Gets the cutoff to consider the dependency between binary
features.
Usage
DependencyBasedStrategyConfiguration$getBinaryCutoff()
Returns
The numeric value of binary cutoff.
Method getRealCutoff()
Gets the cutoff to consider the dependency between real
features.
Usage
DependencyBasedStrategyConfiguration$getRealCutoff()
Returns
The numeric value of real cutoff.
Method setBinaryCutoff()
Sets the cutoff to consider the dependency between binary
features.
Usage
DependencyBasedStrategyConfiguration$setBinaryCutoff(cutoff)
Arguments
cutoffThe new numeric value of binary cutoff.
Method setRealCutoff()
Sets the cutoff to consider the dependency between real
features.
Usage
DependencyBasedStrategyConfiguration$setRealCutoff(cutoff)
Arguments
cutoffThe new numeric value of real cutoff.
Method tiebreak()
The function solves the ties between two (or more) features.
Usage
DependencyBasedStrategyConfiguration$tiebreak(
feature,
clus.candidates,
fea.dep.dist.clus,
corpus,
heuristic,
class,
class.name
)
Arguments
featureA character containing the name of the feature
clus.candidatesA single or numeric vector value to
identify the candidate groups to insert the feature.
fea.dep.dist.clusA list containing the groups chosen for
the features.
corpusA data.frame containing the features of the initial
data.
heuristicThe heuristic used to compute the relevance of each
feature. Must inherit from GenericHeuristic abstract class.
classA character vector containing all the values of the
target class.
class.nameA character value representing the name of the
target class.
Method qualityOfCluster()
The function determines the quality of a cluster.
Usage
DependencyBasedStrategyConfiguration$qualityOfCluster(clusters, metrics)
Arguments
clustersA list with the feature distribution of each
cluster.
metricsA numeric list with the metrics associated to the
cluster (dependency between all features and dependency between the
features and the class).
Returns
A numeric vector of length 1.
Method isImprovingClustering()
The function indicates if clustering is getting better as
the number of them increases.
Usage
DependencyBasedStrategyConfiguration$isImprovingClustering(clusters.deltha)
Arguments
clusters.delthaA numeric vector value with the quality
values of the built clusters.
Returns
A numeric vector of length 1.
Method clone()
The objects of this class are cloneable with this method.
Usage
DependencyBasedStrategyConfiguration$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
StrategyConfiguration,
DependencyBasedStrategy
D2MCS documentation built on Aug. 23, 2022, 5:07 p.m.
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| DependencyBasedStrategyConfiguration | R Documentation |
Custom Strategy Configuration handler for the DependencyBasedStrategy strategy.
Description
Define the default configuration parameters for the DependencyBasedStrategy strategy.
Super class
D2MCS::StrategyConfiguration -> DependencyBasedStrategyConfiguration
Methods
Public methods
Method new()
Method for initializing the object arguments during runtime.
Usage
DependencyBasedStrategyConfiguration$new( binaryCutoff = 0.6, realCutoff = 0.6, tiebreakMethod = "lfdc", metric = "dep.tar" )
Arguments
binaryCutoffThe numeric value of binary cutoff.
realCutoffThe numeric value of real cutoff.
tiebreakMethodThe character value of tie-break method. The two tiebreak methods available are "lfdc" (less dependence cluster with the features) and "ltdc" (less dependence cluster with the target). These methods are used to add the features in the candidate feature clusters.
metricThe character value of the metric to apply the mean to obtain the quality of a cluster. The two metrics available are "dep.tar" (Dependence of cluster features on the target) and "dep.fea" (Dependence between cluster features).
Method minNumClusters()
Function used to return the minimum number of clusters distributions used. By default the minimum is set in 2.
Usage
DependencyBasedStrategyConfiguration$minNumClusters(...)
Arguments
...Further arguments passed down to
minNumClustersfunction.
Returns
A numeric vector of length 1.
Method maxNumClusters()
The function is responsible of returning the maximum number of cluster distributions used. By default the maximum number is set in 50.
Usage
DependencyBasedStrategyConfiguration$maxNumClusters(...)
Arguments
...Further arguments passed down to
maxNumClustersfunction.
Returns
A numeric vector of length 1.
Method getBinaryCutoff()
Gets the cutoff to consider the dependency between binary features.
Usage
DependencyBasedStrategyConfiguration$getBinaryCutoff()
Returns
The numeric value of binary cutoff.
Method getRealCutoff()
Gets the cutoff to consider the dependency between real features.
Usage
DependencyBasedStrategyConfiguration$getRealCutoff()
Returns
The numeric value of real cutoff.
Method setBinaryCutoff()
Sets the cutoff to consider the dependency between binary features.
Usage
DependencyBasedStrategyConfiguration$setBinaryCutoff(cutoff)
Arguments
cutoffThe new numeric value of binary cutoff.
Method setRealCutoff()
Sets the cutoff to consider the dependency between real features.
Usage
DependencyBasedStrategyConfiguration$setRealCutoff(cutoff)
Arguments
cutoffThe new numeric value of real cutoff.
Method tiebreak()
The function solves the ties between two (or more) features.
Usage
DependencyBasedStrategyConfiguration$tiebreak( feature, clus.candidates, fea.dep.dist.clus, corpus, heuristic, class, class.name )
Arguments
featureA character containing the name of the feature
clus.candidatesA single or numeric vector value to identify the candidate groups to insert the feature.
fea.dep.dist.clusA list containing the groups chosen for the features.
corpusA data.frame containing the features of the initial data.
heuristicThe heuristic used to compute the relevance of each feature. Must inherit from GenericHeuristic abstract class.
classA character vector containing all the values of the target class.
class.nameA character value representing the name of the target class.
Method qualityOfCluster()
The function determines the quality of a cluster.
Usage
DependencyBasedStrategyConfiguration$qualityOfCluster(clusters, metrics)
Arguments
clustersA list with the feature distribution of each cluster.
metricsA numeric list with the metrics associated to the cluster (dependency between all features and dependency between the features and the class).
Returns
A numeric vector of length 1.
Method isImprovingClustering()
The function indicates if clustering is getting better as the number of them increases.
Usage
DependencyBasedStrategyConfiguration$isImprovingClustering(clusters.deltha)
Arguments
clusters.delthaA numeric vector value with the quality values of the built clusters.
Returns
A numeric vector of length 1.
Method clone()
The objects of this class are cloneable with this method.
Usage
DependencyBasedStrategyConfiguration$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
StrategyConfiguration,
DependencyBasedStrategy
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