D2MCS (version 1.0.1)

ClassWeightedVoting: Implementation Weighted Voting scheme.

Description

A new implementation of ClassMajorityVoting where each class value has different values (weights).

Super class

D2MCS::SimpleVoting -> ClassWeightedVoting

Methods

Public methods

• ClassWeightedVoting$new()

• ClassWeightedVoting$getWeights()

• ClassWeightedVoting$setWeights()

• ClassWeightedVoting$execute()

• ClassWeightedVoting$clone()

Inherited methods

• D2MCS::SimpleVoting$getCutoff()
• D2MCS::SimpleVoting$getFinalPred()

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Method new()

Method for initializing the object arguments during runtime.

Usage

ClassWeightedVoting$new(cutoff = 0.5, weights = NULL)

Arguments

cutoff

A character vector defining the minimum probability used to perform a positive classification. If is not defined, 0.5 will be used as default value.

weights

A numeric vector with the weights of each cluster. If NULL performance achieved during training will be used as default.

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Method getWeights()

The function returns the weights used to perform the voting scheme.

Usage

ClassWeightedVoting$getWeights()

Returns

A numeric vector.

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Method setWeights()

The function allows changing the value of the weights.

Usage

ClassWeightedVoting$setWeights(weights)

Arguments

weights

A numeric vector containing the new weights.

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Method execute()

The function implements the cluster-weighted majority voting procedure.

Usage

ClassWeightedVoting$execute(predictions, verbose = FALSE)

Arguments

predictions

A ClusterPredictions object containing all the predictions achieved for each cluster.

verbose

A logical value to specify if more verbosity is needed.

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Method clone()

The objects of this class are cloneable with this method.

Usage

ClassWeightedVoting$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

D2MCS, ClassMajorityVoting, ClassWeightedVoting, ProbAverageVoting, ProbAverageWeightedVoting, ProbBasedMethodology